{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0b093f63",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 基础数据处理\n",
    "import os\n",
    "import pandas as pd\n",
    "import sys\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "# 可视化\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "plt.style.use('ggplot')  # 使用ggplot样式\n",
    "\n",
    "# 机器学习\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.impute import SimpleImputer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8d4717ae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "目录下的文件： ['models', 'sample_submit.csv', 'submission.csv', 'testA.csv', 'train.csv', '分类']\n"
     ]
    }
   ],
   "source": [
    "DATA_DIR=\"./深度学习\"\n",
    "print(\"目录下的文件：\", os.listdir(DATA_DIR))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a2d58d28",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "开始加载数据...\n",
      "\n",
      "数据加载成功!\n",
      "训练集: (800000, 47) | 列: ['id', 'loanAmnt', 'term', 'interestRate', 'installment', 'grade', 'subGrade', 'employmentTitle', 'employmentLength', 'homeOwnership', 'annualIncome', 'verificationStatus', 'issueDate', 'isDefault', 'purpose', 'postCode', 'regionCode', 'dti', 'delinquency_2years', 'ficoRangeLow', 'ficoRangeHigh', 'openAcc', 'pubRec', 'pubRecBankruptcies', 'revolBal', 'revolUtil', 'totalAcc', 'initialListStatus', 'applicationType', 'earliesCreditLine', 'title', 'policyCode', 'n0', 'n1', 'n2', 'n3', 'n4', 'n5', 'n6', 'n7', 'n8', 'n9', 'n10', 'n11', 'n12', 'n13', 'n14']\n",
      "测试集: (200000, 46) | 列: ['id', 'loanAmnt', 'term', 'interestRate', 'installment', 'grade', 'subGrade', 'employmentTitle', 'employmentLength', 'homeOwnership', 'annualIncome', 'verificationStatus', 'issueDate', 'purpose', 'postCode', 'regionCode', 'dti', 'delinquency_2years', 'ficoRangeLow', 'ficoRangeHigh', 'openAcc', 'pubRec', 'pubRecBankruptcies', 'revolBal', 'revolUtil', 'totalAcc', 'initialListStatus', 'applicationType', 'earliesCreditLine', 'title', 'policyCode', 'n0', 'n1', 'n2', 'n3', 'n4', 'n5', 'n6', 'n7', 'n8', 'n9', 'n10', 'n11', 'n12', 'n13', 'n14']\n",
      "样本提交文件预览:\n",
      "       id  isDefault\n",
      "0  800000        0.5\n",
      "1  800001        0.5\n",
      "2  800002        0.5\n",
      "3  800003        0.5\n",
      "4  800004        0.5\n",
      "\n",
      "目标列分布:\n",
      "isDefault\n",
      "0    640390\n",
      "1    159610\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# 创建文件路径映射\n",
    "file_paths = {\n",
    "    \"train\": os.path.join(DATA_DIR, \"train.csv\"),\n",
    "    \"test\": os.path.join(DATA_DIR, \"testA.csv\"),\n",
    "    \"sample\": os.path.join(DATA_DIR, \"sample_submit.csv\")\n",
    "}\n",
    "\n",
    "# 检查所有文件是否存在\n",
    "missing_files = []\n",
    "for name, path in file_paths.items():\n",
    "    if not os.path.isfile(path):\n",
    "        missing_files.append(name)\n",
    "\n",
    "if missing_files:\n",
    "    print(f\"\\n错误: 缺少以下文件:\")\n",
    "    for name in missing_files:\n",
    "        print(f\" - {file_paths[name]}\")\n",
    "    sys.exit(1)\n",
    "\n",
    "# 加载数据\n",
    "try:\n",
    "    print(\"\\n开始加载数据...\")\n",
    "    train_df = pd.read_csv(file_paths[\"train\"], engine='pyarrow')\n",
    "    test_df = pd.read_csv(file_paths[\"test\"], engine='pyarrow')\n",
    "    sample_submit = pd.read_csv(file_paths[\"sample\"])\n",
    "    \n",
    "    print(\"\\n数据加载成功!\")\n",
    "    print(f\"训练集: {train_df.shape} | 列: {list(train_df.columns)}\")\n",
    "    print(f\"测试集: {test_df.shape} | 列: {list(test_df.columns)}\")\n",
    "    print(f\"样本提交文件预览:\\n{sample_submit.head()}\")\n",
    "    \n",
    "    # 检查目标列\n",
    "    if \"isDefault\" in train_df.columns:\n",
    "        print(\"\\n目标列分布:\")\n",
    "        print(train_df[\"isDefault\"].value_counts())\n",
    "        \n",
    "except Exception as e:\n",
    "    print(f\"\\n加载过程中出错: {e}\")\n",
    "    print(\"\\n尝试不使用pyarrow引擎...\")\n",
    "    try:\n",
    "        train_df = pd.read_csv(file_paths[\"train\"])\n",
    "        test_df = pd.read_csv(file_paths[\"test\"])\n",
    "        sample_submit = pd.read_csv(file_paths[\"sample\"])\n",
    "        print(\"成功使用默认引擎加载数据!\")\n",
    "    except Exception as e2:\n",
    "        print(f\"仍然出错: {e2}\")\n",
    "        print(\"请检查文件是否损坏或格式不正确\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6015231a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练集特有列: {'isDefault'}\n",
      "\n",
      "训练集信息:\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 800000 entries, 0 to 799999\n",
      "Data columns (total 47 columns):\n",
      " #   Column              Non-Null Count   Dtype  \n",
      "---  ------              --------------   -----  \n",
      " 0   id                  800000 non-null  int64  \n",
      " 1   loanAmnt            800000 non-null  float64\n",
      " 2   term                800000 non-null  int64  \n",
      " 3   interestRate        800000 non-null  float64\n",
      " 4   installment         800000 non-null  float64\n",
      " 5   grade               800000 non-null  object \n",
      " 6   subGrade            800000 non-null  object \n",
      " 7   employmentTitle     799999 non-null  float64\n",
      " 8   employmentLength    800000 non-null  object \n",
      " 9   homeOwnership       800000 non-null  int64  \n",
      " 10  annualIncome        800000 non-null  float64\n",
      " 11  verificationStatus  800000 non-null  int64  \n",
      " 12  issueDate           800000 non-null  object \n",
      " 13  isDefault           800000 non-null  int64  \n",
      " 14  purpose             800000 non-null  int64  \n",
      " 15  postCode            799999 non-null  float64\n",
      " 16  regionCode          800000 non-null  int64  \n",
      " 17  dti                 799761 non-null  float64\n",
      " 18  delinquency_2years  800000 non-null  float64\n",
      " 19  ficoRangeLow        800000 non-null  float64\n",
      " 20  ficoRangeHigh       800000 non-null  float64\n",
      " 21  openAcc             800000 non-null  float64\n",
      " 22  pubRec              800000 non-null  float64\n",
      " 23  pubRecBankruptcies  799595 non-null  float64\n",
      " 24  revolBal            800000 non-null  float64\n",
      " 25  revolUtil           799469 non-null  float64\n",
      " 26  totalAcc            800000 non-null  float64\n",
      " 27  initialListStatus   800000 non-null  int64  \n",
      " 28  applicationType     800000 non-null  int64  \n",
      " 29  earliesCreditLine   800000 non-null  object \n",
      " 30  title               799999 non-null  float64\n",
      " 31  policyCode          800000 non-null  float64\n",
      " 32  n0                  759730 non-null  float64\n",
      " 33  n1                  759730 non-null  float64\n",
      " 34  n2                  759730 non-null  float64\n",
      " 35  n3                  759730 non-null  float64\n",
      " 36  n4                  766761 non-null  float64\n",
      " 37  n5                  759730 non-null  float64\n",
      " 38  n6                  759730 non-null  float64\n",
      " 39  n7                  759730 non-null  float64\n",
      " 40  n8                  759729 non-null  float64\n",
      " 41  n9                  759730 non-null  float64\n",
      " 42  n10                 766761 non-null  float64\n",
      " 43  n11                 730248 non-null  float64\n",
      " 44  n12                 759730 non-null  float64\n",
      " 45  n13                 759730 non-null  float64\n",
      " 46  n14                 759730 non-null  float64\n",
      "dtypes: float64(33), int64(9), object(5)\n",
      "memory usage: 286.9+ MB\n",
      "\n",
      "测试集信息:\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 200000 entries, 0 to 199999\n",
      "Data columns (total 46 columns):\n",
      " #   Column              Non-Null Count   Dtype  \n",
      "---  ------              --------------   -----  \n",
      " 0   id                  200000 non-null  int64  \n",
      " 1   loanAmnt            200000 non-null  float64\n",
      " 2   term                200000 non-null  int64  \n",
      " 3   interestRate        200000 non-null  float64\n",
      " 4   installment         200000 non-null  float64\n",
      " 5   grade               200000 non-null  object \n",
      " 6   subGrade            200000 non-null  object \n",
      " 7   employmentTitle     200000 non-null  float64\n",
      " 8   employmentLength    200000 non-null  object \n",
      " 9   homeOwnership       200000 non-null  int64  \n",
      " 10  annualIncome        200000 non-null  float64\n",
      " 11  verificationStatus  200000 non-null  int64  \n",
      " 12  issueDate           200000 non-null  object \n",
      " 13  purpose             200000 non-null  int64  \n",
      " 14  postCode            200000 non-null  float64\n",
      " 15  regionCode          200000 non-null  int64  \n",
      " 16  dti                 199939 non-null  float64\n",
      " 17  delinquency_2years  200000 non-null  float64\n",
      " 18  ficoRangeLow        200000 non-null  float64\n",
      " 19  ficoRangeHigh       200000 non-null  float64\n",
      " 20  openAcc             200000 non-null  float64\n",
      " 21  pubRec              200000 non-null  float64\n",
      " 22  pubRecBankruptcies  199884 non-null  float64\n",
      " 23  revolBal            200000 non-null  float64\n",
      " 24  revolUtil           199873 non-null  float64\n",
      " 25  totalAcc            200000 non-null  float64\n",
      " 26  initialListStatus   200000 non-null  int64  \n",
      " 27  applicationType     200000 non-null  int64  \n",
      " 28  earliesCreditLine   200000 non-null  object \n",
      " 29  title               200000 non-null  float64\n",
      " 30  policyCode          200000 non-null  float64\n",
      " 31  n0                  189889 non-null  float64\n",
      " 32  n1                  189889 non-null  float64\n",
      " 33  n2                  189889 non-null  float64\n",
      " 34  n3                  189889 non-null  float64\n",
      " 35  n4                  191606 non-null  float64\n",
      " 36  n5                  189889 non-null  float64\n",
      " 37  n6                  189889 non-null  float64\n",
      " 38  n7                  189889 non-null  float64\n",
      " 39  n8                  189889 non-null  float64\n",
      " 40  n9                  189889 non-null  float64\n",
      " 41  n10                 191606 non-null  float64\n",
      " 42  n11                 182425 non-null  float64\n",
      " 43  n12                 189889 non-null  float64\n",
      " 44  n13                 189889 non-null  float64\n",
      " 45  n14                 189889 non-null  float64\n",
      "dtypes: float64(33), int64(8), object(5)\n",
      "memory usage: 70.2+ MB\n",
      "\n",
      "训练集重复值: 0, 测试集重复值: 0\n"
     ]
    }
   ],
   "source": [
    "# 检查训练集和测试集的列差异\n",
    "print(\"训练集特有列:\", set(train_df.columns) - set(test_df.columns))\n",
    "\n",
    "# 检查数据基本信息\n",
    "print(\"\\n训练集信息:\")\n",
    "train_df.info(verbose=True, show_counts=True)  # 显示每列的非空值和数据类型\n",
    "print(\"\\n测试集信息:\")\n",
    "test_df.info(verbose=True, show_counts=True)\n",
    "\n",
    "# 检查重复值\n",
    "print(f\"\\n训练集重复值: {train_df.duplicated().sum()}, 测试集重复值: {test_df.duplicated().sum()}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c5f4f49b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "类别比例: 0=80.05%, 1=19.95%\n"
     ]
    }
   ],
   "source": [
    "# 目标列分布可视化\n",
    "plt.figure(figsize=(8,4))\n",
    "sns.countplot(x='isDefault', data=train_df)\n",
    "plt.title('Class Distribution (0=No Default, 1=Default)')\n",
    "plt.show()\n",
    "\n",
    "# 计算不平衡比例\n",
    "imbalance_ratio = train_df['isDefault'].value_counts(normalize=True)\n",
    "print(f\"类别比例: 0={imbalance_ratio[0]:.2%}, 1={imbalance_ratio[1]:.2%}\")"
   ]
  },
  {
   "cell_type": "raw",
   "id": "8941ee5f",
   "metadata": {},
   "source": [
    "# 选择数值型列（排除ID和分类列）\n",
    "# 数值型特征分析\n",
    "num_cols = train_df.select_dtypes(include=['int64','float64']).columns.tolist()\n",
    "num_cols = [c for c in num_cols if c not in ['id','isDefault']]\n",
    "\n",
    "# 绘制分布图（示例前5个特征）\n",
    "for col in num_cols[:5]:\n",
    "    plt.figure(figsize=(10,4))\n",
    "    sns.histplot(train_df[col], kde=True, bins=30)\n",
    "    plt.title(f'Distribution of {col}')\n",
    "    plt.show()\n",
    "\n",
    "# 关键统计量\n",
    "print(train_df[num_cols].describe().T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0178d82f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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CY+GuhXvuuUeBgYH69NNPtXHjRod1//vf/3Tw4EF17tw510fkFcR//vMfeXh46Omnn1Z0dHSO9ampqUVSQCjIzz83V5OvwYMH6/z58+YjO/OasLAwFVXey5cvX6A+n5dnnnlGqampioyMzHW0TkJCQq7zcxRE3759JUkvvviiwwim06dPa/z48blu48zvNQCUFMxJAABwMGbMGEkXRg4kJiZq9+7dioqKUmpqqlq2bKlPPvnkik8nOHfunDp37qwaNWqoVatWql69ulJSUvT999/r999/V/fu3dWgQQOzfadOnfTLL7/o9ttv18033yybzabGjRvrzjvvvOrjadeunWbMmKFNmzapTZs2OnbsmBYsWKDMzExNnz7dYVh/y5Yt1aFDB61du1YtW7ZUx44d9ffff+vLL79U165dcz1h6NSpkyZPnqwBAwaoZ8+e8vHxkb+/v/7zn//kGdO///1vLV++XAsXLlTDhg11zz33yGKxaNmyZTp06JB69+6thx566KqPPTc+Pj6aNWuW7rvvPrVv31733XefQkNDtXXrVq1cuVKVK1c2h8ZfjXr16mnWrFmKjIxUw4YNddtttyk8PFxpaWmKiYnRjz/+qIoVK+Y6KV1hKsjPPzdXk6+ePXsqKChIx44dU8WKFXXvvfdei0N0UFR579Spkz777DPdfffdatq0qaxWq9q1a6d27do5tHv00Ufz3Me0adMUGRmprVu3atq0aapVq5a6du2q0NBQnTp1SocOHdL69ev12GOP6f3333c61r59++qzzz7Tt99+q4iICN11111KS0vTkiVL1KJFC+3bt8+81SjbLbfcIjc3N40YMUK//fabOcroxRdfdDoOACg2iupZiwCA65uynj2e/Z/NZjPKly9vNGvWzOjfv7/xzTffGBkZGblum/2c9WypqanGpEmTjNtuu82oVq2a4enpaVSoUMFo1aqV8d577zk8q9wwLjyPfeDAgUbVqlUNd3d3Q5LxyCOPOMR2uWeyZz+z/NChQ+ayQ4cOmfv5/fffjbvuusvw9/c3ypYta7Ru3dr49ttvc91XYmKi8fjjjxsVK1Y0bDab0bBhQ2P69OkO+7vUG2+8YdSrV8+w2WyGJKN69ep55iZbRkaGMXXqVKN58+ZG2bJljbJlyxrNmjUz3n333VzzfLkc5Hb8V7J582bjnnvuMSpUqGB4eHgY1apVMwYOHGj89ddfOdr+8MMPhiRj9OjR+d5/tp07dxqPPPKIERoaathsNiMgIMBo2LCh8fjjjxurV692aJtXrgzj8seY2zPtnfn5z54925BkzJ49O8e6guTrYk8//bQhyfjvf/+bZ5vCPO5shZX3vHLy999/Gw8++KARFBRkuLm55egfl36e5PZfQkKC2f7LL7807rjjDqNixYqGh4eHUalSJeOGG24wRo4cafz+++8O7+3M78K5c+eMUaNGGTVq1DBsNptRvXp144UXXjBiY2MNScY999yTY1/z5s0zGjdubJQpU8aM+UrvYxhX9/sCANcDi2Fc5WwwAAAA15nDhw+rZs2aeuSRRzRnzhyXxdGuXTtFRUUpOjpatWvXdlkcyN3333+vW2+9VcOHD9eECRNcHQ4AXBeYkwAAAOAa2Lhxo3788UfddtttFAhc7OjRozmWnTx50pwDpGfPnkUdEgBct5iTAAAAoBC9++67io2N1UcffSR3d/c8J71E0XnmmWe0Y8cOtW7dWhUrVlRsbKy++eYbnTp1SoMHD1aLFi1cHSIAXDcoEgAAABSi119/XbGxsapdu7befvttTkCvAz179lR8fLy+/vprnTp1Sp6enoqIiFC/fv2u2ZNEAKC4Yk4CAAAAAAAgiTkJAAAAAABAFooEAAAAAABAEkUCAAAAAACQhSIBAAAAAACQxNMNXCohIUHp6emuDgMAAAAAUMJZrVYFBARcuV0RxII8pKenKy0tzdVhAAAAAAAgidsNAAAAAABAFooEAAAAAABAEkUCAAAAAACQhSIBAAAAAACQRJEAAAAAAABkoUgAAAAAAAAkUSQAAAAAAABZKBIAAAAAAABJFAkAAAAAAEAWigQAAAAAAEASRQIAAAAAAJDF6uoAkJPH8jWuDsEl0u7u6OoQAAAAAKBUYyQBAAAAAACQRJEAAAAAAABkoUgAAAAAAAAkUSQAAAAAAABZKBIAAAAAAABJFAkAAAAAAEAWigQAAAAAAEASRQIAAAAAAJCFIgEAAAAAAJBEkQAAAAAAAGSxujqApUuXavPmzfrrr79ks9kUHh6uhx9+WMHBwWabqVOnat26dQ7b1alTR6+88or5Oi0tTfPmzdOGDRuUmpqqiIgI9e/fX+XLlzfb2O12zZ49W1u2bJEktWjRQpGRkfL29jbbxMfHa8aMGdq9e7dsNpvatGmjvn37ymr9J1UxMTGaOXOmDhw4IB8fH3Xp0kU9e/aUxWIp9PwAAAAAAFBUXF4k2LNnj7p27apatWopIyNDn332mV5++WW9+eabKlOmjNmuSZMmGjx4sPn64pN2SZozZ462bt2qYcOGydfXV3PnztXEiRM1adIkubldGDDxzjvv6OTJkxo5cqQkafr06ZoyZYqGDx8uScrMzNSECRNUrlw5jRs3TmfOnNHUqVMlSZGRkZKk5ORkjR8/Xg0bNtSECRN07NgxTZs2TZ6enrrzzjuvXaIAAAAAALjGXH67wciRI9WhQwdVq1ZNNWrU0ODBgxUfH6+DBw86tLNarfL39zf/8/HxMdclJydrzZo16tu3rxo1aqSaNWtq6NChiomJ0c6dOyVJsbGx2r59uwYOHKjw8HCFh4friSee0LZt23T06FFJ0o4dOxQbG6uhQ4eqZs2aatSokfr27avVq1crOTlZkhQVFaW0tDQNGTJEoaGhatWqlXr06KEVK1bIMIwiyhoAAAAAAIXP5SMJLpV9Mn5xEUC6MOKgf//+8vb2Vv369fXggw/Kz89PknTw4EFlZGSoUaNGZvvAwECFhoYqOjpaTZo0UXR0tLy8vFSnTh2zTXh4uLy8vLRv3z4FBwcrOjpaoaGhCgwMNNs0btxYaWlpOnjwoCIiIhQdHa0GDRrIw8PDoc38+fMVFxenoKCgHMeUlpamtLQ087XFYlHZsmXNf+MCcgEAAAAArnVdFQkMw9BHH32kevXqKTQ01FzetGlT3XTTTapQoYJOnDihBQsWaNy4cZo4caI8PDyUmJgoq9Wao7Dg5+enxMRESVJiYqJZVChIGx8fH1mtVoc2FStWzLGP7HW5FQmWLl2qxYsXm69r1qypSZMm5dhPtrhcl5Z8VapUcXUIAAAAAFCqXVdFgpkzZyomJkbjxo1zWN66dWvz36GhoapVq5YGDx6sbdu2qVWrVnnuLz/D/w3DcLiCndvV7Py0uZwePXqoe/fuObaPi4tTenp6jvbX1Q+lCB07dszVIQAAAABAiWS1WvO8UO3QrghiyZdZs2Zp69atGjt2rMMTCXITEBCgihUrmieV/v7+Sk9Pl91udxhNkJSUpLp165ptTp8+nWNfSUlJ5kgAf39/HThwwGG93W5XRkaGQ5vsUQXZsvfr7++fa7weHh4OtydcjHkM/kEuAAAAAMC1XD5xoWEYmjlzpjZt2qSXXnop1+H6lzpz5oxOnjypgIAASVJYWJjc3d3NSQolKSEhQTExMQoPD5d0Yf6B5ORkhyLA/v37lZycbBYSwsPDFRMTo4SEBLPNzp075eHhobCwMLPN77//7jACYMeOHWbhAgAAAACA4srlRYKZM2fqxx9/1LBhw1S2bFklJiYqMTFRqampkqSUlBTNnTtX0dHROnHihHbv3q1JkybJ19dXLVu2lCR5eXmpY8eOmjdvnn777TcdOnRIU6ZMUWhoqDmZYUhIiJo0aaLp06crOjpa0dHRmj59upo1a6bg4GBJFyYgDAkJ0bvvvqtDhw7pt99+07x589SpUyd5eXlJktq2bSur1aqpU6cqJiZGmzdv1tKlS9W9e3cm3gMAAAAAFGsWw8VjvHv37p3r8sGDB6tDhw5KTU3V5MmTdejQIZ09e1YBAQFq2LCh7r//flWoUMFsn5qaqo8//lhRUVFKTU1VRESE+vfv79DGbrebtzVIUvPmzdWvXz95e3ubbeLj4zVjxgzt2rVLNptNbdu2VZ8+fRxuF4iJidHMmTN14MABeXt7q0uXLurVq1eBiwRxcXEOTz3I5rF8TYH2U1Kk3d3R1SEAAAAAQInk4eGRr9HvLi8SlGYUCRxRJAAAAACAayO/RQKX324AAAAAAACuDxQJAAAAAACAJIoEAAAAAAAgC0UCAAAAAAAgiSIBAAAAAADIQpEAAAAAAABIokgAAAAAAACyUCQAAAAAAACSKBIAAAAAAIAsFAkAAAAAAIAkigQAAAAAACALRQIAAAAAACCJIgEAAAAAAMhCkQAAAAAAAEiiSAAAAAAAALJQJAAAAAAAAJIoEgAAAAAAgCwUCQAAAAAAgCSKBAAAAAAAIAtFAgAAAAAAIIkiAQAAAAAAyEKRAAAAAAAASKJIAAAAAAAAslAkAAAAAAAAkigSAAAAAACALBQJAAAAAACAJIoEAAAAAAAgC0UCAAAAAAAgiSIBAAAAAADIQpEAAAAAAABIokgAAAAAAACyUCQAAAAAAACSKBIAAAAAAIAsFAkAAAAAAIAkigQAAAAAACALRQIAAAAAACCJIgEAAAAAAMhCkQAAAAAAAEiiSAAAAAAAALJQJAAAAAAAAJIoEgAAAAAAgCwUCQAAAAAAgCSKBAAAAAAAIAtFAgAAAAAAIIkiAQAAAAAAyGJ1dQBLly7V5s2b9ddff8lmsyk8PFwPP/ywgoODzTaGYWjRokVavXq17Ha76tSpo379+qlatWpmm7S0NM2bN08bNmxQamqqIiIi1L9/f5UvX95sY7fbNXv2bG3ZskWS1KJFC0VGRsrb29tsEx8frxkzZmj37t2y2Wxq06aN+vbtK6v1n1TFxMRo5syZOnDggHx8fNSlSxf17NlTFovlWqYKAAAAAIBryuUjCfbs2aOuXbvqlVde0YsvvqjMzEy9/PLLSklJMdssX75cX331lSIjIzVhwgT5+/vr5Zdf1rlz58w2c+bM0ebNmzVs2DCNGzdOKSkpmjhxojIzM80277zzjg4fPqyRI0dq5MiROnz4sKZMmWKuz8zM1IQJE3T+/HmNGzdOw4YN06ZNmzR37lyzTXJyssaPH6+AgABNmDBBkZGR+vLLL7VixYprnCkAAAAAAK4tlxcJRo4cqQ4dOqhatWqqUaOGBg8erPj4eB08eFDShVEEX3/9tXr06KFWrVopNDRUQ4YM0fnz5xUVFSXpwon7mjVr1LdvXzVq1Eg1a9bU0KFDFRMTo507d0qSYmNjtX37dg0cOFDh4eEKDw/XE088oW3btuno0aOSpB07dig2NlZDhw5VzZo11ahRI/Xt21erV69WcnKyJCkqKkppaWkaMmSIQkND1apVK/Xo0UMrVqyQYRguyCAAAAAAAIXD5bcbXCr7ZNzHx0eSdOLECSUmJqpx48ZmGw8PDzVo0ED79u1Tly5ddPDgQWVkZKhRo0Zmm8DAQIWGhio6OlpNmjRRdHS0vLy8VKdOHbNNeHi4vLy8tG/fPgUHBys6OlqhoaEKDAw02zRu3FhpaWk6ePCgIiIiFB0drQYNGsjDw8Ohzfz58xUXF6egoKAcx5SWlqa0tDTztcViUdmyZc1/4wJyAQAAAACudV0VCQzD0EcffaR69eopNDRUkpSYmChJ8vPzc2jr5+en+Ph4s43VajULCxe3yd4+MTExxz7y08bHx0dWq9WhTcWKFXPsI3tdbkWCpUuXavHixebrmjVratKkSTn2ky0u16UlX5UqVVwdAgAAAACUatdVkWDmzJmKiYnRuHHjcqy79Cpzfob257fNxfvO7Wp2ftpcTo8ePdS9e/cc28fFxSk9PT1H++vqh1KEjh075uoQAAAAAKBEslqteV6odmhXBLHky6xZs7R161aNHTvW4YkE/v7+ki5cpQ8ICDCXJyUlmVfw/f39lZ6eLrvd7jCaICkpSXXr1jXbnD59Osf7XrqfAwcOOKy32+3KyMhwaJM9qiBb9n6zY72Uh4eHw+0JF2Meg3+QCwAAAABwLZdPXGgYhmbOnKlNmzbppZdeyjFcPygoSP7+/uYEhJKUnp6uPXv2mAWAsLAwubu7O7RJSEhQTEyMwsPDJV2YfyA5OdmhCLB//34lJyeb+wkPD1dMTIwSEhLMNjt37pSHh4fCwsLMNr///rvDCIAdO3YoICAgX1UZAAAAAACuVy4vEsycOVM//vijhg0bprJlyyoxMVGJiYlKTU2VdGFofrdu3bR06VJt3rxZMTExmjp1qjw9PdW2bVtJkpeXlzp27Kh58+bpt99+06FDhzRlyhSFhoaakxmGhISoSZMmmj59uqKjoxUdHa3p06erWbNmCg4OlnRhAsKQkBC9++67OnTokH777TfNmzdPnTp1kpeXlySpbdu2slqtmjp1qmJiYrR582YtXbpU3bt3Z+I9AAAAAECxZjFcPMa7d+/euS4fPHiwOnToIOnCaINFixZp1apVOnv2rGrXrq1+/fqZkxtKUmpqqj7++GNFRUUpNTVVERER6t+/vypUqGC2sdvt5m0NktS8eXP169dP3t7eZpv4+HjNmDFDu3btks1mU9u2bdWnTx+H2wViYmI0c+ZMHThwQN7e3urSpYt69epV4CJBXFycw1MPsnksX1Og/ZQUaXd3dHUIAAAAAFAieXh45Gv0u8uLBKUZRQJHFAkAAAAA4NrIb5HA5bcbAAAAAACA6wNFAgAAAAAAIIkiAQAAAAAAyEKRAAAAAAAASKJIAAAAAAAAslAkAAAAAAAAkigSAAAAAACALBQJAAAAAACAJIoEAAAAAAAgC0UCAAAAAAAgiSIBAAAAAADIQpEAAAAAAABIokgAAAAAAACyUCQAAAAAAACSKBIAAAAAAIAsFAkAAAAAAIAkigQAAAAAACALRQIAAAAAACCJIgEAAAAAAMhCkQAAAAAAAEiiSAAAAAAAALJQJAAAAAAAAJIoEgAAAAAAgCwUCQAAAAAAgCSKBAAAAAAAIAtFAgAAAAAAIIkiAQAAAAAAyEKRAAAAAAAASKJIAAAAAAAAslAkAAAAAAAAkigSAAAAAACALBQJAAAAAACAJIoEAAAAAAAgC0UCAAAAAAAgiSIBAAAAAADIQpEAAAAAAABIokgAAAAAAACyUCQAAAAAAACSKBIAAAAAAIAsFAkAAAAAAIAkigQAAAAAACCLU0WCxYsX69SpU7muS0hI0OLFi68qKAAAAAAAUPScKhIsWrToskWCRYsWXVVQAAAAAACg6BX67QYpKSmyWq2FvVsAAAAAAHCN5fts/siRIzp8+LD5etu2bfrrr78c2qSmpioqKkqVKlXKdwB79uzRF198oUOHDikhIUHPPvusWrZsaa6fOnWq1q1b57BNnTp19Morr5iv09LSNG/ePG3YsEGpqamKiIhQ//79Vb58ebON3W7X7NmztWXLFklSixYtFBkZKW9vb7NNfHy8ZsyYod27d8tms6lNmzbq27evQ9EjJiZGM2fO1IEDB+Tj46MuXbqoZ8+eslgs+T5mAAAAAACuR/kuEmzevNlhroElS5bk2s5ms2nQoEH5DuD8+fOqUaOGbrnlFr3xxhu5tmnSpIkGDx5svr50pMKcOXO0detWDRs2TL6+vpo7d64mTpyoSZMmyc3twmCJd955RydPntTIkSMlSdOnT9eUKVM0fPhwSVJmZqYmTJigcuXKady4cTpz5oymTp0qSYqMjJQkJScna/z48WrYsKEmTJigY8eOadq0afL09NSdd96Z72MGAAAAAOB6lO8iQefOndW8eXMZhqEXXnhBgwYNUmhoqOPOrFZVrlxZNpst3wE0bdpUTZs2vXyQVqv8/f1zXZecnKw1a9Zo6NChatSokSRp6NChGjRokHbu3KkmTZooNjZW27dv1yuvvKI6depIkp544gm9+OKLOnr0qIKDg7Vjxw7FxsbqvffeU2BgoCSpb9++mjZtmh544AF5eXkpKipKaWlpGjJkiDw8PBQaGqpjx45pxYoV6t69O6MJAAAAAADFWr6LBAEBAQoICJAkjR49WmFhYSpTpsw1C+xie/bsUf/+/eXt7a369evrwQcflJ+fnyTp4MGDysjIMAsEkhQYGKjQ0FBFR0erSZMmio6OlpeXl1kgkKTw8HB5eXlp3759Cg4OVnR0tEJDQ80CgSQ1btxYaWlpOnjwoCIiIhQdHa0GDRrIw8PDoc38+fMVFxenoKCgXONPS0tTWlqa+dpisahs2bLmv3EBuQAAAAAA13JqhsEGDRoUdhx5atq0qW666SZVqFBBJ06c0IIFCzRu3DhNnDhRHh4eSkxMlNVqlY+Pj8N2fn5+SkxMlCQlJiaaRYWCtPHx8ZHVanVoU7FixRz7yF6XV5Fg6dKlDrdq1KxZU5MmTcqxr2xxuaeixKtSpYqrQwAAAACAUs3pxxCsX79eGzZsUFxcnFJTUx3WWSwWTZky5aqDk6TWrVub/w4NDVWtWrU0ePBgbdu2Ta1atcpzO8MwrrhvwzAcrl7ndiU7P22upEePHurevXuOfcTFxSk9PT1H+9L6bIhjx465OgQAAAAAKJGsVmueF6od2jmz82XLlunTTz9VSEiIqlev7jD8/loLCAhQxYoVzRNKf39/paeny263O4wmSEpKUt26dc02p0+fzrGvpKQkcySAv7+/Dhw44LDebrcrIyPDoU32qIJs2fvNa84ESfLw8MgzR/kpZpQW5AIAAAAAXMupIsHq1avVtWtXc9b/onTmzBmdPHnSnB8hLCxM7u7u2rlzpznqICEhQTExMXrooYckXZh/IDk5WQcOHFDt2rUlSfv371dycrJZSAgPD9fnn3+uhIQEc987d+6Uh4eHwsLCzDaffvqp0tPTzScs7NixwyxcAAAAAABQnDlVJEhMTFTLli0LJYCUlBQdP37cfH3ixAkdPnxYPj4+8vHx0cKFC3XjjTfK399fcXFx+vTTT+Xr62u+v5eXlzp27Kh58+bJ19dXPj4+mjdvnkJDQ83JDENCQtSkSRNNnz5dAwYMkCR98MEHatasmYKDgyVdmIAwJCRE7777rh5++GHZ7XbNmzdPnTp1kpeXlySpbdu2WrRokaZOnaoePXro+PHjWrp0qXr16sWkewAAAACAYs9iODHGe/To0br55pvVuXPnqw5g9+7dGjt2bI7l7du314ABAzR58mQdOnRIZ8+eVUBAgBo2bKj7779fFSpUMNumpqbq448/VlRUlFJTUxUREaH+/fs7tLHb7Zo1a5a2bt0qSWrevLn69esnb29vs018fLxmzJihXbt2yWazqW3bturTp4/DrQIxMTGaOXOmDhw4IG9vb3Xp0sXpIkFcXJzDUw+yeSxfU+B9lQRpd3d0dQgAAAAAUCJ5eHjkawS8U0WCP/74Q1OmTNGTTz5pDsVHwVEkcESRAAAAAACujfwWCZy63WDatGk6c+aMRowYIX9/f/n6+jqst1gsmjx5sjO7BgAAAAAALuJUkcDX11flypUr7FgAAAAAAIALOVUkGDNmTCGHAQAAAAAAXM3N1QEAAAAAAIDrg1MjCfbs2XPFNg0aNHBm1wAAAAAAwEWcKhLk9sjCSy1YsMCZXQMAAAAAABdxqkgwevToHMuSkpK0ZcsW7du3T/369bvqwAAAAAAAQNFyqkiQ160EN954oz744ANt375dTZo0uZq4AAAAAABAESv0iQtbtmypDRs2FPZuAQAAAADANVboRYKzZ88qPT29sHcLAAAAAACuMaduN4iPj8+xLC0tTUeOHNH8+fNVp06dqw4MAAAAAAAULaeKBEOGDMlzXXBwsCIjI50OCAAAAAAAuIZTRYJBgwblWGaz2VSxYkXVqlVLbm6FfhcDAAAAAAC4xpwqEnTo0KGQwwAAAAAAAK7mVJEg27lz5xQdHa0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      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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1xpk3b55Gk3K5XI6lS5cCgMa70ENDQ8WLBMOGDauxI0NDW260atUK//77r0bTekEQEBsbiwsXLlSbfurUqXB0dMSaNWs0LqKoVCq89tprGt+Vmrpzwqefflrru9Zv376NX3/91aDyExGR7tj0nYiIDKZuhq1SqaBQKHD+/HkcP34c5eXl6NWrFzZu3Fitl+pNmzYhIiIC06dPxwcffIDevXvD29sbGRkZSE5Oxt9//42TJ09qPKNdk//9739ISkpCv379MHHiRHh5eeHMmTM4fvw4xo8fj+3bt1eb59VXX0VSUhJGjRqFRx99FFKpFCdOnEBaWhoGDBgg3imtS2RkJOLj4/H0009j3Lhx8PDwgLe3N2bNmqXT/DUZO3YsunTpgqNHjyIpKQlRUVF44oknEB8fjzlz5uDw4cO477778O+//yIxMRFjx47F1q1b9S6fKb8HAOjUqRN+++03dO/eHUOGDEF+fj62bt0KhUKBt956S2uv9W5ubnjyySfFpvu6diKn9sorryA9PR0DBgxAq1at4OTkhLNnz+LgwYNo0aIFJk2aJE47bdo0vP3221ixYgX+/PNPtG/fHv/88w/27duHMWPG1NjDfdOmTVFeXo6OHTvikUceQUVFBbZv344bN27ghRdewEMPPaQxfWxsLFQqFZYvX45evXqhV69e6NmzJ7y9vSGXy3H58mUcPHgQEokE/fr106u+c+bMwXPPPYdu3bph3LhxcHR0xC+//IILFy7g4Ycfxg8//KAxfatWrbBy5Uq88sor6Nq1Kx599FF4eXkhKSkJCoUCnTp1qnaRKzIyEitXrsS8efNw3333Yfjw4QgODkZRURGuXr2KI0eOoF+/fvjxxx/1KjsREenJyq+HIyKiegj///5l9T8nJyfB19dX6NatmzBjxgxh3759Gu/TvltBQYGwbNkyoVu3boK7u7vg4uIitGrVShg+fLjw6aefCkVFRdXi1fTu8x9++EHo3bu34OHhIXh5eQmDBw8Wjhw5UuO7sQVBEBISEoTu3bsLzs7OglQqFR599FEhPT1dr/eoC4IgvPPOO0Lbtm0FJycnAYDQsmXLOtddbe9Rr1o+AEKPHj3EcefPnxcefvhhwd/fX3BzcxO6desmrFu3zqjy6fs91ERdn8zMTGHq1KmCv7+/4OzsLHTt2lXYuHFjrfP++eefAgAhMDBQqKio0Cme2tatW4VJkyYJoaGhgru7u9CoUSOhQ4cOwvz584Xs7Oxq058/f14YPny44OHhIbi7uwv9+/cXDh8+XOt71Fu2bCkoFArhhRdeEAIDAwUnJyehbdu2wvvvvy+oVKoay3b+/HnhxRdfFO6//37B09NTcHBwEKRSqdCnTx/hjTfeEM6fP19tntre9a721VdfCZ07dxbc3NwEX19fYfTo0UJycrKwaNEiAYBw6NChavNs2rRJ6Nq1q+Ds7Cz4+fkJU6dOFTIzM2uNd+zYMWHChAlC06ZNBUdHR8HPz0/o3LmzMGfOHOH06dO1lpGIiIwnEYT/74KUiIiIyMK+/PJLTJ8+HTExMViyZIm1i0NERGQTmKgTERGRVSiVSnTt2hUpKSlIS0tDs2bNrF0kIiIim8Bn1ImIiMiijh49ikOHDuHw4cP4+++/MXv2bCbpREREVTBRJyIiIos6ePAgYmNj4evri+eeew4rV660dpGIiIhsCpu+ExEREREREdkQvkediIiIiIiIyIYwUSciIiIiIiKyIUzUiYiIiIiIiGwIE3UiIiIiIiIiG3JP9/qel5cHpVJp7WIQERERERFRA+fg4AAfHx/dpjVzWWyaUqlERUWFtYtBREREREREJGLTdyIiIiIiIiIbwkSdiIiIiIiIyIbYRNP3pKQkJCQkQKFQICgoCNHR0WjXrl2N01dUVGD79u04duwYFAoFfH19MWbMGERERFiw1ERERERERESmZ/VE/cSJE1i/fj1mzJiBNm3a4MCBA1i+fDlWr14NPz8/rfOsXr0a+fn5eO6559CkSRMUFBSgsrLSwiUnIiIiIiIiMj2rJ+qJiYmIiIhAZGQkACA6Ohp//vkn9u/fjylTplSb/ty5c7hw4QI+/PBDeHh4AAACAgIsWmYiIiIiIiIic7Fqoq5UKpGamorRo0drjO/UqRNSUlK0znPmzBm0bt0au3fvxtGjR+Hi4oLu3btj0qRJcHJy0jpPRUWFRu/uEokErq6u4jARERERERGRrbBqol5QUACVSgUvLy+N8V5eXlAoFFrnuXXrFi5dugRHR0e89tprKCgowBdffIGioiK88MILWufZtWsXtm/fLv4dHByMVatWwd/f32R1ISIiIiIiIjIFqzd9B7Tf1a7pTrcgCACAl156CW5ubgDu3DF/9913MWPGDK131ceMGYORI0dWW3ZOTg6USqXR5SciIiIiIiKqjYODg843i62aqHt6esLOzq7a3fP8/Pxqd9nVvL29IZVKxSQdAJo1awZBEJCbm4umTZtWm8fR0RGOjo5al6dO/ImIiIiIiIhsgVXfo+7g4ICQkBAkJydrjE9OTkabNm20ztO2bVvk5eWhtLRUHHfjxg1IJBL4+vqatbxERERERERE5mb1pu8jR47EmjVrEBISgrCwMBw4cAAymQyDBw8GAGzatAlyuRyzZs0CAPTr1w87duzARx99hIkTJ6KgoADffvstBg4cWGNnckRERERERESmEBcXB7lcDgCQSqWIiYkxeQyrJ+rh4eEoLCzEjh07kJeXh+bNm2PevHli2/28vDzIZDJxehcXFyxYsABffvkl5s6di0aNGuGBBx7ApEmTrFUFIiIiIiIiukfI5XKNHNUcJMI9/JB2Tk6OxmvbiIiIiIiIiGozZ84cMVH38/PD6tWrdZrP0dFR587krPqMOhERERERERFpYqJOREREREREZEOYqBMRERERERHZECbqRERERERERDaEiToRERERERGRDWGiTkRERERERGRDmKgTERERERER2RAm6kREREREREQ2hIk6ERERERERkQ1hok5ERERERERkQ5ioExEREREREdkQJupERERERERENoSJOhEREREREZENcbB2AYiIiIiIiIiMFRcXB7lcDgCQSqWIiYmxcokMx0SdiIiIiIiI6j25XA6ZTGbtYpgEm74TERERERER2RAm6kREREREREQ2hE3fiYiIiIiIbEBDesaajMNEnYiIiIiIyAY0pGesyThM1ImIiIiIiIgAOOw6U/dExWUaw7rMoxzTQ69y8Bl1IiIiIiIiIhvCRJ2IiIiIiIjIhjBRJyIiIiIiIrIhTNSJiIiIiIiIbAgTdSIiIiIiIiIbwkSdiIiIiIiIyIYwUSciIiIiIiKyIUzUiYiIiIiIiGwIE3UiIiIiIiIiG8JEnYiIiIiIiMiGMFEnIiIiIiIisiFM1ImIiIiIiIhsiIO1C0BERERERERUG4cdF+ue6HaFxnBd8yjHtTOyVObDRJ2IiIiIiMjM7DZW1D1RkaAxXNc8qqmORpaKbBWbvhMRERERERHZECbqRERERERERDaETd+JiIiIiKjeiYuLg1wuBwBIpVLExMRYuUREpsNEnYiIiIiI6h25XA6ZTGbtYhCZBRN1IiIiIiIiIh35unpoHTYlm0jUk5KSkJCQAIVCgaCgIERHR6NdO+1d5Z8/fx6xsbHVxq9evRrNmjUzd1GJiIiIiIjoHra4/0Szx7B6on7ixAmsX78eM2bMQJs2bXDgwAEsX74cq1evhp+fX43zvffee3BzcxP/9vT0tERxiYiIiIjoHsJn4ckarN7re2JiIiIiIhAZGSneTffz88P+/ftrnc/Lywve3t7iPzs7q1eFiIiIiIgaGPWz8DKZTEzYiczNqnfUlUolUlNTMXr0aI3xnTp1QkpKSq3zvv7666ioqEBQUBDGjh2Ljh071jhtRUUFKioqxL8lEglcXV3FYSIiIiIiqt8sdV5vS/mDLZWlPrLk+tM3llUT9YKCAqhUKnh5eWmM9/LygkKh0DqPj48PnnnmGYSEhECpVOLo0aOIi4vDokWL0L59e63z7Nq1C9u3bxf/Dg4OxqpVq+Dv72+yuhARERERkeXY29trDDdt2tSm49zEVVMVSWSuOpvS7NmzkZOTAwDw9/fH+++/b9BysnHBlMUCoH39ZZs8Ss2xamP1Z9QB7VcXarriEBgYiMDAQPHvsLAwyGQy/PDDDzUm6mPGjMHIkSOrLTsnJwdKpdKYohMRERERkRVUVlZqDN+4ccOm45jj3q256mxKN2/eFF+jZ8z6s697Er1pK4s54qhjOTg46Hyz2KqJuqenJ+zs7KrdPc/Pz692l702YWFhOHbsWI2fOzo6wtHRUetngiDoHIeIiIiIiGyTpc7rDY1jjkS9PuYytlRmS5ZF31hWTdQdHBwQEhKC5ORk9OrVSxyfnJyMnj176ryctLQ0eHt7m6GEREREREREDQt7srd9Vm/6PnLkSKxZswYhISEICwvDgQMHIJPJMHjwYADApk2bIJfLMWvWLADAnj174O/vj+bNm0OpVOLYsWM4deoUXnnlFWtWg4iIiIiIqF5Q92RPtsvqiXp4eDgKCwuxY8cO5OXloXnz5pg3b57Ydj8vL09jI1IqldiwYQPkcjmcnJzQvHlzzJ07F926dbNWFYiIiIiIiIhMxuqJOgBERUUhKipK62czZ87U+HvUqFEYNWqUJYpFRERERERkMVIXH63DdO+xiUSdiIiIiIjoXreoz3xrF4FsBBN1IiIiIiK6Z5Vst6v1c+G25nBd0wOA63iVscUiA/i6NtI6XB8xUSciIiIiIqJ6b/FDT1i7CCbDRJ2IiIiIiGzKrb11pymVJZrDdc3TeLjS2GIRWQwTdSIiIiIiItKb/XfX6p7otlJjWJd5Kie0MKJUDQMTdSIiIiIiK4iLi4NcLgcASKVSxMTEWLlExmuIdSKyBibqRERERERWIJfLIZPJrF0Mk2qIdSKyhrq7LCQiIiIiIiIii2GiTkRERERERGRDmKgTERERERER2RA+o05ERERERNSA2G3JrX2C25Uaw3VOD0A1ydfIUpE+eEediIiIiIiIyIYwUSciIiIiIiKyIWz6TkREREREVANvV6nWYSJzYqJORERERERUgzceXGTtItA9iIk6EREREZGJ/fSrY53TlJRpDusyz+A+FcYUyyiXD9ZdvopSzWFd5gmNsF6dyPykrl5ah6l2TNSJiIiIiIjILBb3e8baRaiXmKgTEREREVG94+Xqq3WYqCFgok5ERERERPXOrMGLrV0EIrPh69mIiIiIiIiIbAgTdSIiIiIiIiIbwqbvRERERA1MXFwc5HI5AEAqlSImJsbKJSIiIn0wUSciIiJqYORyOWQymbWLQUQ2SurirXWYbAcTdSIiIiIiK3Dz8NU6XJ95uvlqHSbbsih8trWLQHVgok5EREREZAXDxsZauwgm99SIhlcnImtgZ3JERERERERENoR31ImoQWOHSkRERERU3zBRJ6IGjR0qEREREVF9w6bvRERERERERDaEiToRERERERGRDWHTdyIiIqJ6ZObJ1DqnKSxTisO5Zco651n7QIjR5SIiItPhHXUiIiIiIiIiG8JEnYiIiIiIiMiGsOk7EdVb3x+cVuc0xaXKKsOyOucZHfGV0eUiIiIiIjIG76gTERERERER2RDeUSciIiIiauDi4uIgl8sBAFKpFDExMVYuERHVhok6ERERUQNj5+4JVZVhIrlcDplMZu1iEJGOmKgTERERNTDuY6dbuwhERGQEPqNOREREREREZENs4o56UlISEhISoFAoEBQUhOjoaLRr167O+S5duoTFixejefPmiI+Pt0BJiai+cXXTPkxEREREZKusnqifOHEC69evx4wZM9CmTRscOHAAy5cvx+rVq+Hn51fjfMXFxVi7di3uv/9+KBQKyxWYiOqVASOsfpgjIiIiItKL1Zu+JyYmIiIiApGRkeLddD8/P+zfv7/W+T777DP07dsX9913n4VKSkRERERERGR+Vr3VpFQqkZqaitGjR2uM79SpE1JSUmqc79ChQ7h16xZefPFF7Nixo844FRUVqKioEP+WSCRwdXUVh4mI1HhMICJzWbJkicbrsRYuXGjlEv2Hx776Q9t3deaYY53zlZVKNIbPHneqdfoeD1bU+rkpWWr7s+R2zjrVj1i2XCerJuoFBQVQqVTw8vLSGO/l5VVjc/YbN25g06ZNiI2Nhb29vU5xdu3ahe3bt4t/BwcHY9WqVfD39ze47ETUMDVt2tTaRSCiBio/P198PZa9vb0Rx5srpivU/+OxzxxyzLJU7d+V6V+7pi3Ovxas001kWyQOAFzBTYvEuomrFolzJ5ZltolbZqhTTbGyccFCccxD3+OsTTy8qe3qgrZxKpUKH3zwASZMmIDAwECdlz9mzBiMHDmy2rJzcnKgVCoNKDERNVQ3btywdhGIqIGqrKzUGLal440tlaXhMM9ptvbvqu476qaJY8k66XZDzvg4AGD6u6raYpnj3m1NdbJULHM9R60tlum3CMvFUcdycHDQ+WaxVRN1T09P2NnZVbt7np+fX+0uOwCUlJTgypUrSEtLw5dffgkAEAQBgiBg0qRJWLBgATp27FhtPkdHRzg6aj+ACYJgfEWIqMHgMYGILMWWjje2VBaqnaW+K0tuE9avk+nTWm2xzJE811QnS8YyB+tvE9aPZdVE3cHBASEhIUhOTkavXr3E8cnJyejZs2e16V1dXfH2229rjNu/fz/+/vtvvPzyywgICDB7mYmIiIiIiIjMyepN30eOHIk1a9YgJCQEYWFhOHDgAGQyGQYPHgwA2LRpE+RyOWbNmgU7Ozu0aNFCY35PT084OjpWG09ERERERERUH1k9UQ8PD0dhYSF27NiBvLw8NG/eHPPmzRPb7ufl5Ykdr1hTXFycRk+tMTExVi4REREREZFuGnlItQ4TkW2yeqIOAFFRUYiKitL62cyZM2udd+LEiZg4caI5iqVBLpfbxAUDIiIisj3Pn/irzmlul5WLw7ll5TrN83H4/UaVi0ht4qgl1i4CEenBXB31EREREREREZEBmKgTERERERER2RAm6kREREREREQ2hIk6ERERERERkQ0xuDO5CxcuICQkBC4uLtU+Ky0tRWpqKtq3b29U4SzFcffBuicqLtUYrmueilERRpaKiIiIiIiI7kUG31GPjY1FRkaG1s+ysrIQGxtrcKGIiIiIGhqJeyNIGnne+efeyNrFISIiG2aW17MplUrY2bFVPREREZGa25ip1i4CERHVE3ol6sXFxSguLhb/VigU1d4tXl5ejiNHjsDb29skBSQiIiIiIiK6l+iVqO/Zswfbt28X/46Pj69x2jFjxhheKiIiIiIiIqJ7lF6JeufOneHi4gJBELBx40YMHToUfn5+GtM4OjqiRYsW9aYjOSIiurfExcVBLpcDAKRSKWJiYqxcIiIiIiJNeiXqYWFhCAsLAwCUlZUhMjISUqnULAWzNb6ublqHiYiofpHL5dUe2yIiIiKyJQZ3JjdhwgRTlsPmxQ4cYe0iEBERERER0T1Ar0T9yJEjei28f//+ek1PREREREREdK/TK1H/6KOP9Fo4E3UiIiIiIiIi/eiVqH/44YfmKgcRERERERERQc9E3d/f31zlICIiMtqMU1/WOU1ZWZE4LCsrqnOez3s/ZXS5iIiIiPRhZ+0CEBEREREREdF/DO71PTY2ttbPJRIJFi5caOjiiYiIiIiIiO5JBt9RFwSh2riCggJcunQJN27c0Po5EREREREREdXO4Dvqixcv1jo+KysL8fHx99x71omIiIiIiIhMweBEvSaBgYF4+OGH8e2332L58uWmXjwREZFRJB7OEKoMExFVFRcXB7lcDgCQSqWIiYmxcomI6F5k8kQdAAICAnD9+nVzLJqIiMgoTuO6WbsIRGTD5HI5ZDKZtYtBRPc4s/T6/uuvv8LHx8cciyYiIiIiIiJq0Ay+o/7RRx9VG6dUKnH16lVkZGTgscceM6pgRERERERERPcigxP18+fPVxvn5OQEf39/jBkzBv369TOqYERERERERET3IoMT9bVr15qyHEREREREZvf1KUmtnxeVaQ7XNT0APNmbryUmItMyS2dyRET3GvYSTERERESmYnSiXlxcjKysLJSXl1f7rH379sYunoioXmAvwURERERkKgYn6pWVlVi3bh2OHDkClUqldZqtW7caXDAiIiIiIiKie5HBr2fbs2cPzp49i+effx4AMH36dDzzzDNo3bo1mjZtivnz55uskERERERERET3CoMT9aNHj2r07h4aGorIyEgsX74c/v7+WnuFJyIiIiKyZc4eUrg08oNLIz84e0itXRwiukcZ3PT91q1baNWqFSSSOz1hVlRUiJ8NHjwYX331FaZMmWJ8CYmIbMCGo9Nq/byoVFllWFbn9ADw+ENfGV0uIiIyrZ7jFlq7CEREht9Rd3FxgVKphEQigYeHB3JycsTPnJycUFRUZJICEhEREREREd1LDE7UAwMDkZ2dDQAICwvDnj17kJubi/z8fOzevRuBgYEmKyQRERERERHRvcLgpu/h4eHIysoCAEycOBGLFi3CCy+8cGehDg545ZVXTFNCIiIiIiIionuIwYl6VFSUOBwcHIx3330Xp0+fhkQiQadOnXhHnYjuKS7u2oeJiIiIiPSlV6L+xhtvYNasWWjevDmOHDmCbt26oVGjRgAAPz8/DBs2zCyFJPOIi4uDXC4HAEilUsTExFi5RET1V69HDL7uSURERESkQa9n1K9evYrS0lIAwEcffYRbt26ZpVBkGXK5HDKZDDKZTEzYiYiIiIiIyLr0ugXk5eWFtLQ03HfffSYtRFJSEhISEqBQKBAUFITo6Gi0a9dO67SXLl3Cxo0bkZmZibKyMvj7+2PQoEEYOXKkSctEREREREREZA16Jerdu3fHF198gQ0bNgAAYmNjYWdX8035r7/+us5lnjhxAuvXr8eMGTPQpk0bHDhwAMuXL8fq1avh5+dXbXpnZ2dERUWhZcuWcHZ2xqVLl7Bu3Tq4uLhg0KBB+lTHJrE5OhERERER0b1Nr0T9qaeeQlBQEK5du4ZDhw6hQ4cO8PT0NKoAiYmJiIiIQGRkJAAgOjoaf/75J/bv348pU6ZUmz44OBjBwcHi3wEBAfjtt99w8eLFBpGoq5ujExERERER0b1Jr0TdwcEBw4cPBwAcOnQI48ePR2hoqMHBlUolUlNTMXr0aI3xnTp1QkpKik7LSEtLQ0pKCiZNmmRwORoiyfcf1z1RcaHGcF3zCKOfN7JUREREREREVBeDuyneunWr0cELCgqgUqng5eWlMd7LywsKhaLWeZ977jkUFBSgsrISEyZMEO/Ia1NRUYGKigrxb4lEAldXV3HYHEy1XHOVzxC2VBYic7Hkds59qn7g90TWZKntj9u5cRribwfrVD9iNcQ6WTKWLdfJqPcJVVRU4PDhwzh//jwKCwsxY8YMNG3aFKdPn0aLFi3QuHFjnZajrdB1VWTJkiUoLS3FP//8g02bNqFJkybo16+f1ml37dqF7du3i38HBwdj1apV8Pf3BwDk6FRK/TRt2tSg+ezt7TWGDV3ODYPmqp2hZSGqTyy5nXOfqh/4PZFuks2yVO3b3xULxWmoTH+WpH39meMMs6ZYpn9sUlucfy1Yp5vItkgcALiCmxaJdRNXLRLnTizLbBO3zFCnmmJl44KF4piHvsdZgxP1goICxMbGIiMjA97e3lAoFCgpKQEAnD59Gn/++SdmzJhR6zI8PT1hZ2dX7e55fn5+tbvsdwsICAAAtGjRAvn5+fjuu+9qTNTHjBmj0Su8+iJATk4OlEqlcVcranDjhvYfAYfdO2udr7KwUGM455O1dcZSjhqrX+EMVFOdiBoSS27n3KfqB35PZE2W2v64nRtH+/ozxxlmTbEcLRTHknWy1zLOHHEAwPR3VbXFMse925rqZKlYer3r28hYpt8iLBdHHcvBwUG8WVwXg/e2b7/9FsXFxVixYgVatmyp0fFbhw4dsHv37rqDOzggJCQEycnJ6NWrlzg+OTkZPXv21LksgiBAqVTW+LmjoyMcHbUfwARB0DmOPsy1XF1j6bJzSl2dtA7rE4eoobH2vku2h98TWZOltr97azs3fQrTEH877q06WWabMEfyXFOdLBnLHKy/TVg/lsGJ+u+//46pU6ciJCQEKpVK4zNfX1/k5ubqtJyRI0dizZo1CAkJQVhYGA4cOACZTIbBgwcDADZt2gS5XI5Zs2YBAH788Uf4+fmhWbNmAO68V/2HH37AsGHDDK3KPWvhgPutXQQiIiIiIiK6i8GJeklJSY237ZVKZbXkvSbh4eEoLCzEjh07kJeXh+bNm2PevHnisvPy8jReVyYIAjZv3ozs7GzY2dmhSZMmmDp1aoN4NRsA+P5/J3d3DxMREREREdG9weBEPSAgAP/88w86duxY7bPLly8jMDBQ52VFRUUhKipK62czZ87U+HvYsGEN+u55bER/axeBiIiIiIiIrMjgRL1fv37YvXs3mjdvjm7dugG400nb5cuXsW/fPowZM8ZkhSQiooYtLi4OcrkcACCVShETE2PlEhERERFZj8GJ+qhRo5CSkoK3334b7u7uAIBly5ahsLAQXbp0wfDhw01WSCIiatjkcrnGY05ERERE9zKDE3UHBwfMmzcPJ06cwO+//478/Hw0atQI3bt3R3h4OOzszNVZPxEREREREVHDZdTLECUSCfr27Yu+ffuaqjxERERERERE9zSDE3WlUokrV64gLy8PAODj44PWrVvDwcGo3J+IiIiIiIjonqZ3Vq1SqbBlyxb8+OOPKCsr0/jMxcUFw4cPx4QJE9j0nYiIAADTf3urzmnKy/LFYVlZvk7zfNHrdaPKRURERGSr9E7U33vvPZw6dQpBQUHo3r07/P39IQgCZDIZzpw5g507d+LmzZuYPXu2OcpLRERERERE1KDplagnJyfj1KlTmDBhAsaPH1/t8ylTpmDbtm3YsWMHIiIicP/995usoERERERERET3Ar3apx89ehRt27bVmqSrTZw4EW3btsXRo0eNLhwREd0bJB6OQKM7/yQejtYuDhEREZFV6XVH/cqVKxg2bFid0/Xt2xf79u0zuFBERHRvcRzf2tpFMLm4uDjI5XIAgFQqRUxMjJVLRERERPWFXol6Xl4eAgMD65wuMDBQPDkhIiK6F8nlcshkMmsXg4iIiOohvZq+l5SUwMXFpc7pnJ2dq/UIT0RERERERER1M9s71ARBMNeiiYiIiIiIiBosvV/PtmHDBri5udU6TXFxscEFIiIiqg+e/nVbrZ+Xlt0Wh2Vlt+ucHgDW9ZlodLmIiIio/tMrUffz89P5eTs/Pz+DCkRERERE9QM7TSQiMg+9EvW1a9eaqxxEREREVM+w00QiIvPQu+k7ERER1U3i4QKhyjARERGRrgxO1OVyOUpLS8XXtVVWViIxMRFpaWno1KkTIiIiTFZIIiKi+sZ5bD9rF4GIiIjqKYMT9c8++wx+fn6YMWMGAGDHjh3YsWMH3NzccPLkSTg4OOChhx4yWUGJiIiIyLJeOpld6+f5ZZXisLysss7pAeCDBwKMLhcRUUNn8OvZ0tLS0LFjR/Hvn3/+GSNGjMBXX32FQYMGISkpySQFJCIiIiIiIrqXGJyoFxUVwdvbGwCQkZEBhUKBAQMGAAB69+6NrKwsU5SPiIiIiIiI6J5icNN3Nzc35OfnAwAuXrwIDw8PtGjRAgAgkUigVCpNU0IiIiIiskkSdy/xro/E3cuqZSEiakgMTtRbt26N3bt3w97eHnv37kWnTp3Ez27dugWpVGqSAhIRERGRbfIc+6K1i0BE1CAZ3PR90qRJuHXrFuLj45Gfn4+xY8eKn50+fRqtW7c2SQGJiIiIiIiI7iUG31Fv1aoVPvroI2RmZqJJkyZwc3MTP4uKikKTJk1MUkAiIiIiIiKie4leibpMJoO3tzccHBwgk8kAAJ6eniguLkZxcbE4nfpZdSIiIiIiIiLSj16J+syZM7Fs2TKEhoZi5syZdU6/detWgwtGREREREREdC/SK1F//vnn0bhxY3GYiMwvLi4OcrkcACCVShETE2PlEhERERERkTnplair35N+9zARmY9cLhcfNSEiIiIioobP4F7fiYiIiIiIiMj0mKgTERERERER2RCDX89GRMZL3jejzmnKS8qqDMt0mqfTsM+NKhcREREREVkP76gTERERERER2RAm6kREREREREQ2hE3fiWxcIzcJAKHKMBERERERNWRM1Ils3JTBTtYuAhERERERWRATdWpQ4uLiIJfLAQBSqRQxMTFWLhEREREREZF+mKhTgyKXyyGTyaxdDCIiIiIiIoOxMzkiIiIiIiIiG2ITd9STkpKQkJAAhUKBoKAgREdHo127dlqnPXXqFPbv34/09HQolUoEBQVhwoQJ6NKli2ULTURERERERGQGVk/UT5w4gfXr12PGjBlo06YNDhw4gOXLl2P16tXw8/OrNv3FixfRqVMnTJ48Ge7u7jh06BBWrVqF5cuXIzg42Ao1IEvJ3v1cndNUFhdXGc7VaZ6AUZ8YVS4iIiIiIiJTsnrT98TERERERCAyMlK8m+7n54f9+/drnT46OhqjRo1CaGgomjZtiilTpqBp06Y4e/ashUtOREREREREZHpWvaOuVCqRmpqK0aNHa4zv1KkTUlJSdFqGSqVCSUkJPDw8apymoqICFRUV4t8SiQSurq7isDmYa7nWjGXJOllSQ6xXQ6yTpTTEfbehaojHPm4TdDdu5/VDQ1x/rFP9iNUQ62TJWLZcJ6sm6gUFBVCpVPDy8tIY7+XlBYVCodMyEhMTUVZWhgceeKDGaXbt2oXt27eLfwcHB2PVqlXw9/cHAOToX/Q6NW3aVOt4S8W6YaE4lnRLh2m8XCVah2tjzXr9aablWvu7qs8sue74PRnHUuuP2wTpJtksS9W+TVyxUBxAt19fU8WyFNOfJWmvkznO+mqKZfo33miL868F63QT2RaJAwBXcNMisW7iqkXi3IllmW3ilhnqVFOsbFywUBzz0PfYZ/Vn1AHtVxd0ueJw/PhxfPfdd3jttdeqJftVjRkzBiNHjqy27JycHCiVSrOshBs3tP8IWDJWfY1jjBcHuuo9T32ol74aYp0sxZLrjt+TcRrisc/QWEuWLIFcLgcASKVSLFy40JTFIividl4/aK+TeU6ztcdytFAcS9bJ3kJxAMD0d1W1xTLHvdua6mSpWOZ6jlpbLNNvEZaLo47l4OAg3iyui1UTdU9PT9jZ2VW7e56fn19r4g3c6YTuk08+wcsvv4xOnTrVOq2joyMcHbUfwARB0KvMujLXcnWNZY6d05J1sqSGWK+GWCdLsfa+S7qz1PqrD9uEXC6HTPbf3RNuWw0Ht3NzMP1ZUkNcf/dWnSyzTVjy/Ly+5wLW3yasH8uqnck5ODggJCQEycmazcWSk5PRpk2bGuc7fvw41q5di5deegndunUzdzGJiIiIiIiILMbqvb6PHDkSP//8Mw4ePIiMjAysX78eMpkMgwcPBgBs2rQJH374oTi9Okl/4oknEBYWBoVCAYVCgeIqr+UiIiIiIiIiqq+s/ox6eHg4CgsLsWPHDuTl5aF58+aYN2+e2HY/Ly9PoznfgQMHUFlZiS+++AJffPGFOL5///6YOXOmxctPRERkTk+fTKxzmtKy/y5Wy8qK65xn3QMja/2ciIiIrMvqiToAREVFISoqSutndyffixcvtkCJiIiIiIiIiKzDJhJ1IiIiMpzE3Q1ClWEiIiKq35ioExER1XPOYyOtXQQiIiIyIat3JkdERERERERE/2GiTkRERERERGRDmKgTERERERER2RAm6kREREREREQ2hIk6ERERERERkQ1hr+9EZHFxcXGQy+UAAKlUipiYGCuXiIiIiIjIdjBRJyKLk8vlkMlk1i4GEREREZFNYtN3IiIiIiIiIhvCO+pEZHI/7Y+u9fOSksoqw7I6pweAwUPWG1coIiIiIqJ6gnfUiYiIiIiIiGwIE3UiIiIiIiIiG8Km70RkcW5u2oeJiIiIiIiJOhFZwbCh9tYuAhERERGRzWKiTkQivt+ciIiIiMj6mKgTkYjvNyciIiIisj52JkdERERERERkQ5ioExEREREREdkQNn0nukecSHqqzmnKSiqqDMvqnCc86kujy0VERERERJp4R52IiIiIiIjIhvCOOhGJ3N0kAIQqw0REREREZGlM1IlINCaKhwQiIiIiImtj03ciIiIiIiIiG8JEnYiIiIiIiMiGsJ0rEVE9ExcXB7lcDgCQSqWIiYmxcomIiIiIyJSYqBMR1TNyuRwymczaxSAiIiIiM2HTdyIiIiIiIiIbwkSdiIiIiIiIyIaw6TsRkQ1578RTdU5TUFZRZVim0zz/C//SqHIRERERkeXwjjoRERERERGRDeEddSKiesbRXQJAqDJMRERERA0JE3Uionqm7RgeuomIiIgaMp7tERFRjfjOdiIiIiLLY6JOREQ14jvbiYiIiCyPnckRERERERER2RAm6kREREREREQ2hE3fiYjuUS+crvv960VV3tmeWyarc56PevJ97URERETGYqJORERE9zR2mkhERLbGJhL1pKQkJCQkQKFQICgoCNHR0WjXrp3WafPy8vDNN98gNTUVN2/exLBhwxAdHW3ZAhMR3SPsPCRQ/f872+08+M52apjYaSIREdkaqz+jfuLECaxfvx5jx47FqlWr0K5dOyxfvrzGH8yKigp4enpi7NixaNmypYVLS0R0b3Gb4ACPaY7wmOYItwk2cW2XiIiIqMGzeqKemJiIiIgIREZGinfT/fz8sH//fq3TBwQEYNq0aejfvz/c3NwsXFoiIiIiIiIi87Lq7RGlUonU1FSMHj1aY3ynTp2QkpJisjgVFRWoqPivQySJRAJXV1dx2BzMtVxrxrJknSypIdarIW4TrFP9iMU61Y9YDfG4V5NnfvmtzmlKysrE4dyyMjx74nSt03/Wt5fR5dJVQ9wmGuL21xDXH+tUP2I1xDpZMpYt18mqiXpBQQFUKhW8vLw0xnt5eUGhUJgszq5du7B9+3bx7+DgYKxatQr+/v4AgByTRfpP06ZNtY63VKwbFopjSbfMtFxr1utPMy3XUnWy5LpjnepHLNapfsSy9vG8vtO+/pItGOuKheIA5vj1tf72Z/qzJO11MsdZX02xTN/HgrY4/1qwTjeRbZE4AHAFNy0S6yauWiTOnViW2SZumaFONcXKxgULxTEPfY99NvHAobarC6a8ujFmzBiMHDmy2rJzcnKgVCrNshJu3ND+I2DJWPU1jqU1xHo1xG2CdaofsVin+hGrIR73jCFx99A6XBNuE/UnlqVor5N5TrO1x3K0UBxL1sneQnEAwPR3VbXFMse925rqZKlY5nqOWlss028RloujjuXg4CDeLK6LVRN1T09P2NnZVbt7np+fX+0uuzEcHR3h6Kj9ACYIgsniWGK5usYyx85pyTpZUkOsl6XqZO3tvD7HaaixWKf6EashHveM4TJmgl7Tc5uoP7G0M/1ZUkNcf/dWnSyzTVjy/Ly+5wLW3yasH8uqnck5ODggJCQEycmazcWSk5PRpk0bK5WKiIiIiIiIyHqs3vR95MiRWLNmDUJCQhAWFoYDBw5AJpNh8ODBAIBNmzZBLpdj1qxZ4jzp6ekAgNLSUhQUFCA9PR0ODg4ICgqyRhXoHhUXFwe5XA4AkEqliImJsXKJiIiIiIioIbB6oh4eHo7CwkLs2LEDeXl5aN68OebNmye23c/Ly6v2TvXXX39dHE5NTcXx48fh7++PtWvXWrTsdG+Ty+XVtk0iIiIiIiJjWT1RB4CoqChERUVp/WzmzJnVxm3bts3cRSIiIiIiIiKyCptI1IlszZU9z9Q5TUVxaZXh3DrnaT3iM6PLRUREREREDZ9VO5MjIiIiIiIiIk1M1ImIiIiIiIhsCJu+ExnI002idZiIiIiIiMgYTNSJDDR9kLO1i0BERERERA0Qm74TERERERER2RDeUSezi4uLg1wuBwBIpVLExMRYuURERERERES2i4k6mZ1cLodMJrN2MYiIiIiIiOoFNn0nIiIiIiIisiG8o05Gq9j+Qq2fC7eLqgzn1jk9ADiO/8jochEREREREdVHvKNOREREREREZEOYqBMRERERERHZEDZ9J7PzcbUDoKoyTEREVDu+MYSIiO5lTNTJ7OY/6GbtIhARUT3DN4YQEdG9jLc3iYiIiIiIiGwI76gTERGRRT1z4lid05SWlYrDuWWlOs3zWfiDRpWLiIjIVvCOOhEREREREZEN4R11IiIi0gk7eCMiIrIMJupERESkE0t28CZxd9c6TEREdC9gok5EREQ2x3nMKGsXgYiIyGqYqBMREREA4JmTB2r9vLSsRBzOLSupc3oA+OyBQUaXi4iI6F7DzuSIiIiIiIiIbAgTdSIiIiIiIiIbwqbvREREpBN28EZERGQZTNSJiIhIJ85jh1m7CHQP4+sBiehewkSdiIiIiGyeJV8PSERkbXxGnYiIiIiIiMiG8I46EREREVnVa79W1DmNvEzQGK5rnvg+jkaXi4jIWnhHnYiIiIiIiMiG8I46EREREdk8ew8frcNERA0RE3UiIiIisnm+Y+dbuwhERBbDpu9ERERERERENoSJOhEREREREZENYaJOREREREREZEOYqBMRERERERHZECbqRERERERERDaEiToRERERERGRDWGiTkRERERERGRDmKgTERERERER2RAHaxcAAJKSkpCQkACFQoGgoCBER0ejXbt2NU5/4cIFfP3118jIyICPjw8eeeQRDBkyxIIlJiIiIiIiIjIPq99RP3HiBNavX4+xY8di1apVaNeuHZYvXw6ZTKZ1+uzsbKxYsQLt2rXDqlWrMGbMGHz11Vf49ddfLVxyIiIiIiIiItOzeqKemJiIiIgIREZGinfT/fz8sH//fq3T79+/H35+foiOjkZQUBAiIyMxcOBA/PDDDxYuOREREREREZHpWbXpu1KpRGpqKkaPHq0xvlOnTkhJSdE6z7///otOnTppjOvSpQsOHToEpVIJB4fqVaqoqEBFRYX4t0Qigaurqzitg5/UyJpo4eiodbSDn59lYvk1tUwcABL/EJOHctASy9XP9HEAwFFLLHff1haJ42mGODXF8vYNtUgcAJBKLRPLz0JxAKCxj2ViNfM2fZyaYoV4WaZOrb2CTB6n5lhNLBIHAFp7+1skVmtvX4vEuRPL9L+H2uvkY/I4NcfyskicEO9GJo9Tcyx3i8QBgGBvV4vEauUtMUMc7ae5jU2/SWg9RZJ6m+c0W1ssL297M8SpHshDark6ufqao07at7NKf3Nsf0K1cZIA08cRatjOJQEuZohV/Yuy83czeRwAsNMSy97f9MdZibY4fmY4SPx/LG25ao3TC4JQfSuyELlcjueeew5xcXFo06aNOH7nzp04cuQI3n///WrzzJ49G/3798fYsWPFcSkpKYiJicGnn34KH5/qP/7btm3D9u3bxb/79u2L2bNnm7g2RERERERERMazetN34M4dbl3G1fSZ+lpDTfOMGTMG69evF/89/fTTGnfYdVVSUoI33ngDJSUles9ri3EsGash1smSsVin+hGrIdbJkrFYp/oRqyHWyZKxWKf6Eash1smSsVin+hGrIdbJkrHMHceqTd89PT1hZ2cHhUKhMT4/Px9eXtqbHHh7e1ebvqCgAPb29vDw8NA6j6OjY41NuvQhCALS0tJg7kYIlopjyVgNsU6WjMU61Y9YDbFOlozFOtWPWA2xTpaMxTrVj1gNsU6WjMU61Y9YDbFOloxl7jhWvaPu4OCAkJAQJCcna4xPTk7WaApf1X333Vdt+j///BMhISF6tfknIiIiIiIiskVWb/o+cuRI/Pzzzzh48CAyMjKwfv16yGQyDB48GACwadMmfPjhh+L0Q4YMgUwmE9+jfvDgQRw8eBAPP/ywtapAREREREREZDJWvwUdHh6OwsJC7NixA3l5eWjevDnmzZsHf/87venm5eVpvFM9ICAA8+bNw9dff42kpCT4+Phg2rRp6NOnj9nL6ujoiPHjx5ukGb0txLFkrIZYJ0vGYp3qR6yGWCdLxmKd6keshlgnS8ZinepHrIZYJ0vGYp3qR6yGWCdLxjJ3HKv2+k5EREREREREmqze9J2IiIiIiIiI/sNEnYiIiIiIiMiGMFEnIiIiIiIisiFM1ImIiIiIiIhsCBN1IiIiIiIiIhvCRJ0sqqG9ZMAa9Wlo6xBomHUiUqusrLR2EcyC+y0REZH5MFEnkTlPuuRyOZRKJSQSCVQqldniAEB5eTkEQUBJSQkAmC1eVlYWjh8/DqVSaZblV1VcXIyCggIoFApIJBKzx7OU4uJiADB7nbKzs3H16lWzxrA2c+9Xag0pObNEXa5du4YvvvhC3NYbAvW2pl5/5tr2ysvLAcDsx3IyXk5ODs6fP2+R30PAsschS15os8Q2XlJSgrKyMrPHsSZzbx+W2P5UKpVFt3NLxrLEdl5UVISCggKzxwGAGzdu4MyZM2ZZNhN1HclkMhw6dAiJiYlITk42ezxLJZuWSACzs7Px/PPPIy4uDkqlEnZ2dmarT0ZGBj766CMsXrwYS5cuxaVLl8wSLz09HXPmzEFBQQEcHBwAmO8gd/36daxatQpxcXF48cUXcezYMbPEAYDMzEzs3btXPEE2p/T0dLz33nvIzMw0e5w33njDYom6pU5YFQoFLl++jN9//x0AzLZfFRcXIzc3Fzdu3AAAi1xsM7fCwkIAd+pizpOT9PR0vP7665BKpXBzcwNgvmN5ZmYmfvnlF7Msu6qsrCxs2rQJ7777LrZt24YbN27Azs7O5OsxIyMDH374IWJjY/HWW28hLS0NdnbmP2Uxd1J28+ZNbN26FWvWrMHPP/9s1liW2k8VCgVee+01fPzxx7h48aLZ1mF2djZSUlIAmH/fVbPEhbby8nKUl5eL50fmlJWVhYULF+LEiRNmTdZv376NW7duQS6Xm307zM7Oxq5du7Bhwwbs3bsXgHm2j9TUVKxatQqVlZVmv7mQlZWFdevWYeXKlVi3bp1Zz8ksdSPt5s2bOHLkCCorK82aB6hjvfLKK9izZ4/Zk3X177xcLjfL8h3MstQG5tq1a1i1ahWkUilu376NGzdu4LnnnkP//v3NEi8jIwPbt29HXl4elEolHn/8cbRt2xYqlcqkB/Hr16/j888/R3FxMW7evIlnnnkGDz74oMmWr1ZaWgp/f3/cunULixYtQmxsrJjcmtL169exaNEi9OvXD82aNcPNmzexYsUKrFixAoGBgSaLc/XqVcTExODhhx/GiBEjqn1uyu/p+vXrWLhwIQYOHIiOHTsiJSUFn332GTp37gxPT08IgmCSHwxBEFBWVoalS5dCLpfj9u3bGDt2LOzt7U1Qi+rS09Mxb948jBgxAs2aNatWFlP9CKanpyMmJgaDBw/GQw89ZJJl1kZ9wurh4YFnn30W7du3N8s6vHbtGj744AMIggCZTIbWrVtj4cKFJj/JU5+kKhQKAEDbtm3x/PPPm+VkMjMzE+np6ejbt6/Jl11VRkYGXn31VQwePBjTp08XT+hMfeJV9Tgxfvx4cbypj+PAnav58+bNQ1lZGYqKihAVFWXS5atdu3YNsbGx6NatG8rLy5GSkoL09HTMnDkTjRo1Mmkc9bE8KCgIV69exYYNGzB37lw4OTmZ/Pu6fv06tmzZgpdeegnOzs6orKw0y3579epVLF++HMHBwbCzs8O6deugUqkwePBgk8e6efMm/vrrL/Tp08ek301NmjZtisLCQqxZswazZs1Cu3bt4OjoaLLvKisrCwsWLIC9vT1mzZqFzp07m23fVUtPT8fcuXMxfvx4jQttptx/MzIy8O233yI/Px8lJSV44okn0K1bN7PV6+jRo7h27Rq2bNkCe3t79OnTB05OTuLnpoh77do1fPLJJygrK4NcLseECRMwbNgws9Tn2rVrWLZsGYKDg1FaWooDBw7gxo0b4rHdVNLT07Fo0SIMHjxY49hgju/p2rVrWLJkCTp27IhmzZrh6NGjEAQBzzzzjEnjAHd+d19++WWEhoaK5+bm+I26ffs2YmNj4eLigpKSEnE9mms7v3DhAhQKBfbs2QNBEPDII4/Aw8MDwH831UwRt+o55pAhQ4xenjZM1OuQnZ2NVatWITw8HBMmTEBpaSn279+PvXv3onPnzvDy8jL5CYMlkk1LJYAqlQoSiQSenp54/PHH8dlnnyE2NhZxcXEA7pxMNGnSxOg4+fn5+OyzzzBw4EA8/vjjAIC8vDxkZWXh4sWLCAwMNEmdMjIyMHfuXERFReGxxx6DIAg4ePAgsrKy4ODggPDwcLRs2dLo+gB3rnKuXbsWkZGReOyxxwAAAQEBuHr1KsrKypCdnQ0/Pz+TfE8SiQQuLi7o0qULKisrsWvXLpSVlWHixIlm+RGPiYnBqFGjMGnSJAB3muKVlpbCx8fHZPtTZmYmYmJiMGzYMEyZMgWVlZU4f/48CgoK4OnpiU6dOpkkzt3MfcKakZGB2NhYDBo0CP369UN+fj7ee+89/PPPPwgLCzNBDe7IzMxEbGwsBg4ciA4dOkAmk2Hfvn3Yv3+/yX+QLJVoyuVyfPTRRwgODsaRI0cgkUjw1FNPmfyEPycnB7GxsejatSumTp0KANi6dSsyMzOhUCgwaNAgdO/eHe7u7kbHKi4uxrZt29ClSxcEBQXhyy+/hEqlwrBhw4xedlVyuRxr1qxBRESEWKdTp05h8+bNyMvLM1kymJ2djXfeeQeDBw/GlClTAAAHDhzAhQsXYG9vj+LiYjFpMlW8lStXQiaTIS4uDjExMWZJ1m/evIlVq1ahf//+mDRpEuzs7PDpp58iNzfXZDHU1PtTSUkJSkpKMGjQIJOus6pUKhVcXFzQpEkTzJ07F59++inWrl2L2bNno3379khJSUHbtm2NipGfn4+vvvoKwcHBaNSoEb755hs8/vjj6NKlS72+0JaRkYFFixahb9++6NmzJy5evIj3338f8fHxCAgIMEmMu7Vv3x7AnZYjn3zyCVQqFfr37y+uP2PX4/Xr1xEbG4v+/fujX79+OHnyJDZv3ox+/frB09PT6PJXJZPJ8M477+DBBx/EY489hvLycpw9exZbtmxBRkYGgoKCTBJHnYwNGTJEPL8EzHPRNTs7G/Hx8Rg4cKB4nPXx8cGtW7dMHk+hUGDdunXo1KkTbt26hSVLlmDhwoVmSdbLy8thZ2cHDw8PnDhxAgAQEREBJycns1wYDQkJwYgRIxAWFobVq1dDEASMGzcOLi4uqKio0DivNZS6dcrgwYPxxBNPoLKyEr/++ivy8vLg4OCAhx56yCTHXSbqtRAEAYcOHULLli0xYcIEODk5wcnJCWFhYdi/f7/JfxwslWyqT1YtkQDa2dmhefPm8Pb2hpeXF1544QV88MEHiI2Nha+vL1xcXPDYY4/BxcXFqDgymQwqlQq9e/cWx/n4+MDT0xMZGRnGVkOUnp4OlUqFoKAgFBUVIT4+HoIgoKKiAvb29khISMArr7yCHj16GP1dFRUVITg4GIMGDRLHnThxAn///bd4Ytm3b1+MHTsWfn5+RtVLfVAuKSlBu3bt0KdPH8THx8Pe3h6TJ0/G4cOH0aVLF3h7exsVR6FQICYmBmFhYWKSvm7dOqSnp6OgoABNmzbF008/DX9/f6Prs3nzZjg5OaFnz54AgPj4eOTm5qKgoAAFBQXo27cvxo8fb5ILReqY5j5hLSwsxMcff4z+/ftj8uTJAIDGjRujefPmUCgUOHbsmHixzRjFxcX49ttv0bdvX/EYUVZWhj/++AOpqalGLVtbrO+++87siSZw5yq7n58fRowYgdzcXHz00UcAICbrpjo5yc3NhZubG9zc3HDx4kVs3boV9vb28PHxQWBgINauXYuRI0fi0UcfhaOjo1HHibKyMvj4+KBt27bo2rUrXFxcsH79egDAsGHDTPY7dfnyZfj4+CAiIkJcT71798aWLVtw7do1tGjRwugYwJ3kJSQkBEOHDhXH3bp1CykpKZg/fz7Kysowfvx49OvXz+i6lZWVITExESEhIZg8eTISExOxePFiLF682KTJuiAI2L9/Pzp16oTx48eL21h5eTnS0tKwfPlyhISEoE+fPmjVqpVRsUpLS/Hdd9+hV69eCAgIwMaNG1FZWYmoqCizJOt2dnZwcXGBUqnEv//+i9dffx1Lly7FmjVr0KxZM9y+fRsxMTFGxc7Pz4e9vT1GjRoFFxcX7Nu3Dxs2bAAAdOnSxUQ1+U9OTg6WLFli1gttBQUF+OKLL9CvXz9MmzYNABAZGYl58+bh4MGDmDRpktnuNp4+fRrvvPMObt++jc8//xzu7u74448/EBQUhOHDhxu83Pz8fHz66ad48MEH8cQTTwAAmjVrhqtXryI3NxfFxcVwdXWFl5eX0XUQBAGnT5+Gr68vRo8eDQBwcnJCy5Ytcfv2bZSWlhodA7hzvhwXF4fOnTvj8ccfR2VlJTZu3IibN28iJycHAwcORJcuXUxyI00QBPz6669o27YtxowZI46/efMmUlJSEBMTAx8fHwwePBj333+/0b9V6enpkEqlGDRoEFxcXLB69WqzJes+Pj7o2rUrwsPDceTIERw9ehQODg4YNGgQrl69ipCQEJPEUXNxccHJkyfx+OOP49lnn8Wnn34KV1dXZGVlwcPDA08++aRR+5YgCEhKSoKdnR1CQ0NRXl6O+Ph4FBUVobS0FAqFAkeOHMG0adOMvoHCRL0GWVlZyMvLw3333QdHR0eNqy+hoaGwt7dHYWEhfHx8TBYzJycHgiCgT58+4jhzJJsFBQUIDg7WaG5nrgQQAJRKJfLz8/HPP/9gwIABePPNNzF37lxcuHABy5Ytg4uLi8EHhIKCAuTk5KCsrAxPPvmkuEMolUo4ODjAzc3NpM1c+vXrh8LCQqxbtw47duzAfffdh8cffxxSqRRlZWXis4dvvfUWGjdubFAMdZ3c3d0xffp08TGBo0ePYteuXXjhhRfQpk0bZGZm4u2330ZYWBgGDBhgdN2AOyc9t27dQlRUFGbPno33338fv/32GwCgY8eORi/f29sb7dq1Q35+Pg4fPoyffvoJrq6uCA8Ph7u7O/bs2YNly5ZhyZIlBrfsyMrKQllZGSZPnoz169dj27ZtkMlkCAgIwMyZMyGVSpGZmYkVK1bA1dUV06dPN7pegGVOWBs1aoTu3buLd0UAICEhASkpKSgpKUFRURE2bdqEV155BaGhoQaf6JWXl8PNzU28sCAIApydndGlSxecOnUKwH/7mPpzQ/ev0tJSSKVStGnTBl26dDFLopmVlYXCwkJ07twZLi4uaNOmjVjujz/+GMCdZF39rLWh8dT7rp+fH+bMmYMvvvgC7777Llq3bo3nn38ejRo1gp2dHTp27Ig1a9aga9euBu9X6liurq6YNGmS+Bulbo1QdR0Cd+6gFRcXG3zn29vbG+Hh4WjatCmA/zo6srOz0/qsq6HH9G7duiEwMBBSqRQAsG/fPuzduxdPPvkkvL29kZaWhg8//BBNmjRBaGioQXVRc3Z2RmBgINq0aYPw8HBIpVJ88803Gsm6sSerWVlZKC4uxsMPP4wbN26I39POnTtx/PhxDBkyBF5eXti3bx8yMjIwZ84cgy8OlJWVQalUIjg4GH5+fnjggQfg7u4ubgumStbV255EIhFPsBs1aoTz58+jR48eWLBgAZ577jn89ddfmD59utExW7Rogccee0y8Qzp06FCtybqpEguZTGbWC23l5eXIzc1FaWmp+LihuuyNGzdGUVERANOcs9y4cQO5ubnicSYsLAweHh4oKyvDM888AxcXF7zzzjtwdXXFggULDI5TXl6OwsJCdOnSReMRyoSEBPz1119ifVu2bImJEycafUFKIpEgLCwMSqVSbNKsUqkQGBgId3d3k/QpUF5eDoVCgeDgYOTn5+PChQv4/vvvUVFRgZYtW6JRo0Y4cOAA0tLS8Oijjxp9viyRSDB06FBcu3ZN3Gd2796NAwcOYNy4cQgMDMS+ffvw7bffYtGiRWK9DdWxY0c4ODiI5xPqc767k3VDW1uUlZXB2dlZ/E3NyclBVlYWpk2bhs8//xzHjh1DUlISZDIZPvvsM9jb25vswkBAQAACAgJw/fp1REREwN3dHe+++y6cnJwwb948o/ctiUSCSZMmoaSkBHv27MHmzZsRGBiI2bNnw9vbGyqVCjExMdi8eTMWLVpkVCx2JqeFuvOpzMxMdO7cGWPHjgXw33MN6g2pamdR//77r9Fxg4ODMWrUKNx3330ayzd1stmqVSuMGzdOTCTVCeCzzz6LN954A//73/9w5MgR/P333wbHUHcSoVKp4ODggHbt2ol12LZtG+zt7SGVSrFx40aDO1DJyMhAfHw8tm7dir1794onbeqYwJ0Tsaonkdu3b8fZs2cNqpP6+xg2bBimT58OJycnjB49Gv7+/rC3t4ebmxsGDhwIBwcHgztIU9dpy5Yt+Pbbb8X1UllZCXd3d8TFxeHBBx9EQEAAunbtitDQUFy4cMGgWIIgVNumnZ2dce7cOQBAnz590KZNG2RlZSE0NNSoHyFBEMT1N3fuXPj5+eGTTz6Bt7c3XnzxRYwYMQIDBgxAbGwslEoltm/fDkD/7V2976akpKBZs2aYOnUqSktL4efnh6effhqtWrWCp6cn2rVrhxkzZuDw4cPIzs42Sacz6m1efcIKAAsWLIAgCPjrr78wcOBAg09Yq66/sWPHign077//jp9//hkvv/wy5s+fjw8//BC+vr7YunUrAP3Xn3qb8Pb2xvDhwxEeHl6tjhUVFQCgkUwYc1ySSqWIiopCz5494ejoiKioKEydOhXr16/Hvn37xGVXVlaKncDpQ71NpKWloVGjRujRo4f42QMPPIAXXngBBw8exJdffgngzjpQP8epD/W+u3nzZqxfvx6tW7fGhAkTxLsjXl5e4n7Wr18/+Pn5iR1j6avqcWLTpk0afX44Oztj6NChmDJlirgOAeCbb77Bnj17DO7ksOoFQXVSYW9vX631xo8//oiMjAyDjunqfUjdyqWiogL5+fmYP38+hgwZgl69emH06NHw8/Mz+LgH3PmO1R2eDR06FOHh4ZBIJGjTpo34SNPixYtRVlYGOzs7lJeXIycnR+/Oj9Tbnro1gvqEODs7GxkZGZg3bx6mT5+O8ePHY+7cuTh9+rTBLVZSU1Px2muvoby8HA8++CAeeOABAHd+r5588kls2bIFSUlJYgKjUqkM6vyo6u/uzp07xfV4//33i9vW2rVrIQgCQkNDsXv3bvzxxx8GdxylXn7VZsz33Xcfhg8fjpYtW2LDhg3ib9Y333xj8O97Ve3atcO0adNw9epVvPPOO3BxccFLL72EF154Ac899xxefPFFJCYm4p9//tH72Kf+nvz9/fHwww9rnLcAd27O3P17ZGhHYurtLysrSxzn4uIi9i8B3HnszMnJCeXl5bh165ZBsdR18vDwwIABA8Rzy8OHD2P79u2YOXMmYmJiMG3aNOTm5oq/j4aQy+U4fvw4Dh48iEaNGuHhhx8GoHmRxt7eXuOO+pkzZ/Sul7pOUqkU48ePh7e3N5YuXQpBEPDKK68gOjoazz//PB555BGcO3fOqJtp6jr99NNPKC4uFrcJhUKBwsJCvPnmm5g4cSL69euHmJgYXLt2zeBOravGun37tsaF4tDQUMyZMwd5eXlYsmSJeG6+b98+vb+z1NRUvPrqq5DJZOL2fP/99yMrKwsuLi6YNWsW5HI5bt68icjISDg6OhrcKam6Tj///DNkMhmAO+e0jRo1Qnp6OgDgzz//hJubG8rLy/H3338b3MGcOtb+/fshkUgQHR2Nxo0bIyAgANOmTUOTJk3g4uICNzc3zJw5ExcvXjT4d16Nd9Tvon4WZejQodWew5RIJKisrERpaSlUKhWcnZ0BAJs2bcLu3buxbt06vZuclpWVoaysDNevX0fTpk3RoUMHABB7RQS0J5vBwcHo3r273nGuXbuGwMBAuLq6AriTfLq5uSEuLk48OAQEBIgJoD53ajMzM3HkyBFERkaKSZ26Dn5+fvjnn3+QnJyM8+fP480334SdnZ3Yo+/8+fN1jgP894z9kCFDMGjQIPj6+oqx7j5BVP8AbtmyBbt27cLKlSuNqhMADBkyBG3bthVPKNVXDO3s7ODl5WVQ066qdRo8eDCkUqnGD0/V71sQBJSWlsLZ2dmgu0pZWVn48ccfIZfL0aZNG/HHrmnTpuI2/PHHH+PmzZuYPHkytm/fDhcXF0RHR+t9t6dqrNDQUIwePRqvvPIKvv32W7Ru3VpjXbm6uqJJkyYGNVtT77vDhg3D0KFDIQgCWrVqhRdffBFpaWlis331d1VZWQl/f394enoalGjeve+qD84dOnTAxYsXAVQ/YfX390fnzp31SmKqrr+wsDA88sgj4mcBAQGYP38+mjdvLv7AtW3bFmlpaXrXJysrC/v27YNcLkeHDh3EJpBVT4Cq9gorkUiwfv165OfnY/bs2XrFuvt4pG5Cqj6uqr+/qneFv/nmG7i6umL8+PE6d0Z59zZxN3XzbQBiM3iJRIL9+/djzZo1Otfn7n1X3dKqa9euaNasWbVHRvLy8uDp6WnQM5S1HSfUnJycxM6bNmzYgOPHj+Py5ctYuXKlzuuu6rHP19dXo/VEXcfYd955R+f61HSMValUcHR0xMSJEzVaOty+fRve3t4GP3+qbX9SHw/s7e3Rvn17PPbYY/j222+xePFivPnmm9i6davYP4n6d78utW17AQEBeOqpp+Dh4SFeHKusrESLFi0MerwoPT0dsbGxGDBggNgSAfiv1Yt6X/76668B3GlivXv3bigUCjz77LM6P69Z27bn7u6O1NRUvP3220hJSUFsbCwCAwMxf/58fPPNN1i5cqXO666mbQL473gUGhqK4cOHY+/evdi4cSP27duHc+fOGdy6TH1Munr1Kpo1a4Zu3brBw8MDCQkJGDlypMbvVL9+/bB582akpKTo1SJG/T31798fHh4e4oXQyspKcf9SqVTIz88X50lISICbmxsiIiL0+t2ouv3dfS7btGlTVFZW4quvvsLvv/+Od999F4mJiXj//fcxe/bsahdodanTgAEDNLZdlUqF0NBQLFu2TDxH6dGjB3744QeDb2xdv34da9asQVBQEKRSKSIiIsTP7OzsxJ7LlUqluE2rj0lr167V+WZD1e/J29sb3t7eqKyshJ+fH3r27AkPDw9xOxwwYAC2bduG8+fPG/QYxt11qroOvb29MXbsWI2ODGUyGYKDgw16ZO/uWNrOU4ODgzFnzhysXr0acXFxCAoKwoEDB/Dee+/pHEe9/gYOHKixzv39/fHHH38AuPN7W1paivbt2+Pff//FDz/8gOHDh+t9fnl3nSIjI8XPGjduDLlcjq+++gpnz57F6tWr8ffff2PNmjWws7PD2LFj9dqn7o7l7OwMiUSC5557DpcuXdI49gJ3HgVp3Lix0S0tmKhXoe48ZPjw4eLznwBw7tw5BAQEIDAwEHZ2duIB1dHREdu3b8ePP/6IZcuW6Z2kZ2VlYefOnbhy5Qqys7Ph5OSErl27Yvjw4QgNDdW4Am1MsllXnG7duokbq6EJoFKpxIcffojU1FT8+uuv6NatG8LCwsQDflBQEDZs2AA/Pz/MnTtXbC63aNEivZ9PLyoqwrp16/Dggw9qfE9Vm6yqD6IVFRWQSqXYt28ffvjhB6xYsQLBwcEmqVOLFi2qtXQ4duwYHB0d9X7GWpc6VSWRSJCQkIAbN27o/eOQnp6OpUuXok2bNnB0dMSmTZvEXjGbNWuG8vJy/O9//8Pt27cxb948hISEwMfHBxs2bMD48eP1ughxd6ytW7dCEASMGTNG7PylKpVKBScnJ/F5L12bIWvbdyUSCf744w80b95co+8C9fKuXbtm8PPpNe1To0ePRvPmzZGYmIj4+Hj8888/Rp2w3r3+Nm/eDABish4UFFRtG1QoFGjevLnYZE2X9Xd3nA0bNkCpVOKRRx7R+CFTX+AD7lygPHDgABYuXKjbSvt/Na27ESNGoHXr1gBMk2jqcjwHIPZ8rFKpsGbNGri5uWHZsmU6/7jWtO+qj0HaOoVKSkpCaWmp3hfZ9DlOODk5YdCgQfjll19w48YNxMfH6/wceW3HPnUc9d2WoqIiODg4IDExUTzG6ppE1xZHvd2p16M67v79+1FSUmJQ09na9id7e3sxljpZ37RpE5599lnY2dlh0aJFOu+3tW17fn5+CAoKEi9OqffR33//He7u7hr7mDGx1Em6uk7qZH3Dhg04efIkrl27hpUrV+qcpNe1nbdo0QJKpRKZmZmYN2+euH8tX74cMplM53VX1++uOiFzcHBAaGgooqKi8O6770Imk+Gtt94yqDPXu49JDg4O6N69OyZOnIgXX3yx2vSGXGjT9XuSSCTiMU7daiE+Pl6vhKK27a9Vq1YICQnBypUr4e3tjddffx1+fn7iRXh91l9ddaq6ftStsby8vAy6uaC+SBQZGYlHHnlEPN8+e/YsBEFAjx494ODgIJ5TuLi4YPfu3dizZw9WrFih8/G8pjp16NABzZs3F/dP9evFioqKIJVKdT6v1KVOZ86cgSAI6Nmzp0YrPDs7Oxw/fhwSiaRaUmhMLABiSzP14ywvvvgiYmJicP36daxYsUJ85KkutW0TLVq0QHl5Od577z1cvHgRcXFx8PHxwfvvv4/k5GQMHDhQr+b8NdXp9OnTcHV1RceOHfHWW2/B09MTb7zxBry9vdGvXz9IJBK0bNlS7yRdW6zffvsNLi4uWjsnvnDhAnx9fXU+9tWEifr/k8lkWLJkCbp166axce3YsQMHDhwQn92RSCRwcnKCh4cHPv30U1y8eBFLly7VuyME9WtaevToITZ3P3z4ME6dOoW1a9fi2WefFZu3lpeXw9fX16BkU5846voZkgA6ODjggQceQN++fdGiRQtcunQJn332GU6fPo0OHTogMjISjz76KHr27KmxwxtysqVQKJCXl4dJkyZVu9sHaN7xcXZ2RkJCAlxcXBAbG6vX91Rbndq0aYMhQ4aIca5cuSJ2kLF48WK9L9roUif18B9//IEzZ87g5MmTiImJ0at32KtXr2LBggUYMWIEJk+eDJVKhfXr10Mul6O0tFRsEaBSqTBr1ixxffXv37/aj4ahsfLz88Vnl6qeJKpUKnz33Xe4cuWK2BGNLkmmrvuu2q1bt3Do0CEcOnQIS5Ys0ftCUU371K+//op3330XkyZNgoeHh9EnrLV9V+Xl5eK6q3pxatu2bfjzzz8RGxur84+QrnGAO8ciV1dXfPfdd0hMTNT72Ffb8ejDDz/UOB4Zk2jqu00AwN9//w0XFxfxLoKuatp3q178VH9HZ8+exenTp3Hq1CksXLgQvr6+OsepLZa240RlZSV27NiB9PR0vdYdoNuxT51MeHl5YfPmzSgpKcGSJUtMfoxVx0lNTcWRI0dw5MgRLF68WO8TVV2286rfXVhYGBo1agRnZ2fExsaiefPmOsXR51wCuNMM/uDBg0hKSkJcXJxeJ6o1xdqzZw/kcjmmTp2q0Rph+PDh+OWXX3Dz5k289dZbem0TdW3n3t7emDhxIpo1ayZeAFW3UtDnjpI+24S6l+WioiKsWLHCoA4Nazuer1q1Cs8//3y1zqD0vdCmy/dU9TE0qVSKhIQEJCQk6F2vura/mJgYhIaGIiIiAlFRUWjVqpX4fVbt0dyYOuXm5uKxxx7TuFBsZ2eH77//HleuXBHf5qCroqIifP7553jwwQcxdepUcZnff/89Nm/eLLZE7dGjB5ycnODu7o5169bh5s2beh2TaqpTYmIiFAoFpkyZovG7qm4WXlRUJD6yaso6SSQSMYFOTU3FqVOn8OOPPyI2Nlavljf6rD/gTlJ9+PBhODk5YcmSJTr/Hta2TeTl5eHRRx9FWVkZ/vnnH8ydO1c8TsyaNQulpaV6HfvqqlOnTp1w//33Y+zYsQgPD0ezZs3EfUzfV8Dqsv7Ky8vF9XflyhWcOnUK+/fvx5IlS4zuS4DPqP8/lUqFgIAAVFRU4NKlSwDufAl79+7Fs88+K77rWRAEFBQUICMjAxcuXMCKFSsMStIXLFiA/v3746mnnsKAAQPE52nVnQJ988034nNFbm5uSEhIwJYtW/RKNnWNc/PmTQB3rrZ+/vnnSEpKwssvv6z360Fat26N7du3w93dHRMnTsTq1asRGBiIzz//XEyITPEscHp6OmQyGdq1ayde1axKIpGgrKwMGRkZaNWqFRwcHLB06VKDruLWVKcNGzZgwYIF2L9/P86cOYMLFy6Izf0MvdOjS53Uz2WqX/+kz1VcbQdROzs7FBQU4Pz583jjjTfw8ccfIzAwEDNnzqy2vvRJ0uuKNXfuXKxYsQJHjhwBcOfK43vvvYeff/5Z4wCuC133XeDOXfSvv/5aPNnX98Sutn1q8uTJcHFxwQ8//ID7778fCxYsEPdV9XOWup6w6vJdVV1/ly5dwocffoiDBw9i/vz5OvdAq0uc5cuX4/Dhw+I8ycnJ2LNnj0FJuj7HI5VKhZ07dyI9PV3v70qfbUIQBCQnJ+Ovv/7CwoUL9W5Src++KwgCbty4ofe+q2+s1NRUMUFbtWqVQQmMLse+rKwseHt7o6ysDMuWLRNbRJg6ztmzZ/HPP/8gNTXVoGOsrtu5en8C7pxcqrcJXZN0QL9tLyMjA5s2bcKJEycQGxur9/dUU6xt27ZptJRTNwf+8ssvcfnyZSxatEjvWLVte+rhjh07anTiZWineLpuewUFBfjnn3+wdOlSg5P02o7nTk5OWL9+vXhM+v333/Hpp58iKSkJL730ks4X2nT5ntTrysHBAXv27MGOHTv0vvBVW6y9e/fimWeeQWBgINq2bYsnn3zSqA7daqtT9+7dNVrB/Pbbb1i/fj2SkpLw6quv6t2STaFQQC6Xo1evXuI55P79+7F161axE9CffvoJv/32G1QqlXiOvmLFCr2OSTXVSf1WkqpJ+q+//oovvvgCP/74I2bPnq13K0pd63TmzBnk5+fj4MGDOHv2rEHHP31iAXf29atXr2Lx4sV6/R7Wtk106dIFjo6OeOaZZ7Bw4ULx90+lUsHNzU3vC6+11WnatGkQBAGXL19Gs2bNxGOuri0M9Yml7btKSkrCqVOnEBsba5LXNTNR/38BAQF46aWXoFQqkZCQgE8//RSJiYl46aWXNO4sSyQSlJaWYtasWYiPj9f7S1CfMHTt2hVTpkyBvb29Rsc24eHhGDx4MDIzM8WkzNPTU+9kU584f//9NyorK6FQKAxKANXUd8737NmD8vJy+Pj4IDMzE02bNoW/vz9OnjyJOXPmICEhwaiE3d/fH3Z2dmLv09ruHB48eBDr169Hly5d8OGHH+p1oqVLnQICAhAUFIRTp07h7bffFt9ba+hOqUudDh06hJ07d6JDhw6YM2eO3nVSH0SVSqXGQfTs2bPo3bs3HnnkEaSlpeHkyZNG95haV6yHH34Y2dnZ2Llzp/i8mqenJxYtWqT3tqfrvgvcuVI8efJko072a9unIiIicPPmTbi5ucHPz0/czvU9YdV3/dnZ2cHb21vv9adLnJycHOzcuRMZGRlo3rw5goOD9T6B1Pd4BNx5vkulUhmUaOqzTdy6dQudOnUyONHUZ9+9//778cYbbxj8KjNdY23atAkSiQSTJ082+GS8rmPfyZMn8fLLLyM7O1vvu7P6xDl16hTi4+ORmZmJ1157zaBjrD7bubrzIQ8PD7z11lt6rz99tr2KigpMnDgRCxcuNOh70hZrz549ePnll8W7ZHevg5UrVxr0XdW27amH1dueoR0Wqumy7c2ZMwd//PEH3nzzTYPOVww5R5LL5cjKytL7HEmf70ndwsvQ41Ft21/Xrl3FiyouLi5iB2iGdPyoT50KCwvFV94Z8l2lpqYiJycHHTp0EMvavXt3LFy4EFFRUXjiiSegVCqxc+dO3Lx5E08++SRWr15tkt8NdZ3u7osgPz/fqIuuutZp165dUCgUGDlyJObPn2/QcUKf9ZeVlYWWLVti7ty5em9/uqy/0NBQjQs1hvbyXludhg4diieeeAK3b99GYmKi3h3D6hNL23c1evRog4/p2jBRr6Jp06aIjo5GeXk5jh07hlGjRqFz584avWNv2bIFy5cvR+fOnQ3qzEbbCYNEIhF/JABg0KBBaNWqldiT6aBBg7B27VqDrurrEic5ORn29vZ46KGHDEoAq7rvvvvEZ7w++eQTnD9/Hi+//DJeeOEFPPvss3jqqafQrVs3o3qJ9vf3h5ubG44ePYqcnBxxfNXkPycnB61bt4a/v7/Rr9CrrU4zZszAU089heHDhxv1jnFd6pSdnY2QkBA4Ojrq/RwjUP0g+sknn2DPnj149dVXMX78eERGRmLBggXIysrC5cuXDa6LLrEiIiLw5ptv4ubNm7h+/Trat2+P6OhogzuI0mXf3bp1K+Lj4yGVSvVuLQLotk8NGTIEQUFB+PPPP8XPDaHv+gsLC8OUKVM07taZMo76PdatW7fGggULDL7zp8vxSL3ufHx8MGXKFIN/7HQ9ni9atAilpaUG77/67LtOTk5GvapK11jBwcEQBAGOjo4GxwJqP/Y988wzePrpp/HMM88Y/CpKXeKoj7FRUVF6P1Kkps/+9M8//wAABg8ebPbj0VtvvQUvLy+jOhu6O9YjjzyCzp07V5vOyckJw4cPNyipAPTbzk3x7vm6ziWmTZuG++67z+D9yZBzpIEDBxp8oa2u70kdc+zYsfj4448N3va0xaq6/al/k7Zu3Yply5bh9u3bBt840bVOkZGRePHFFw2uU0BAAOzt7cVXxQqCAF9fX7Rr1w4qlQotWrTAAw88AIlEAg8PD/Tq1cvg95rrWqeoqCi88sorBl+g1LVOKpUKnp6eaNKkid53nfWNJQgCXFxc4OjoaPBrPGtbf6ZoUatPncLDwyEIgsG/G/rEqvpdBQYG6v1YW22YqN8lMDAQM2bMQLt27fD333/j4sWLYnOJrVu34ocffsCrr75qkhOGHTt2iD8Qd7OzsxOfDb27J0hTx1E/76V+D7Qx+vTpA3t7e0yePFm82q1O/Js0aYKoqCijfoCAO+tjxowZ+PPPP8XeeIH/mn1u2rQJp06dQv/+/U3yOrva6tS0aVMxObNEnQYMGGBUnaoeRI8fPy4eRNWv/1J3CmTsMzX6xFKfaOnaSVhN6tp3ExIS8Nprr4mdOOlLn33K2AQJsNz60yeOg4ODQduGoevO3NvEDz/8gDfeeMOo5NlS+64+sQYOHGiRY19kZKTRxz5d4pjiGKvrdq7+bTf2pNLcx6PaYlXdv6rWw5h3FFtyOwfqPpcYOnSoUTcVDDlHUr+G1VC1fU/Af48QGJqQ1Rartu3PmO+rrjqptz9jzi3VF4mOHDmCnJwcjfKqt+msrCz4+/ub5LdX1zoZcsNETZ86GXterk8sY+qkVtP6k0gkJkvWdalTZmYm/P39je7MzZLflTZM1LVo0qQJnnrqKQiCgJ07dyItLQ27d+9GQkIC4uLi9H5m6G5NmzbFtGnTIJFINH4g1K8+ys3NhZOTk9iLoDFXOy0RR009/6hRo9CkSRNMnz4drVq1MulVNLWePXsiOjoav/zyC95++2189NFH+Pzzz/H+++/j0KFDePXVVw2+olpVQ6xTTT/gDg4O+Omnn1BSUmLQ8/zWjgU0nH1XzVLrT5c4+naWczdLrzs1c28TgOX2XUvGstSxz5LHWH32J1Mkm5bY9rTFunv/MpWGtu1Z45hU2/dkzIWUumKZc/urrU6m2P58fX0xffp0nDt3TuMiEQAUFxfj22+/xaFDhzBhwgSTJJpAw6qTra0/U2jo668qiWCOX8MG4saNG/j6669x5coVFBUVYdmyZSb9Yb1x4wa++uorCIKAcePGib0db9y4EefOncPcuXNN0nzCUnHUFAoFFi5ciPDwcEyaNMlky9Xm8uXLSEhIwK1bt+Ds7Iw2bdogIiJC51dJ6Koh1qnqdjFlyhQkJydj27ZtWLp0qcHNI20hljpeQ9h3tcUz5/qzRBxLr7uqcc25TQCW23ctGctSxz5LHmMb2vFIW6zCwkI8+eST1XosN4WGtu1Z45hkie/p7ljm3v7MWSeVSoWff/4ZX375JZo0aYKwsDDY29tDLpcjNTUV8+bNM+u+W9/rxPVXf2LdjYl6HbKysvDtt99i8uTJRjWzqom2EwZ1b5+m6ojAknHUjh49inXr1mHRokUmvWOqTdVXxZhTQ6yTNU4gLRELaDj7btV4ljzZMmccS687NXNvE4Dl9l1LxrLUsc+Sx9iGdjyqKjMzE1u3bsUTTzxh1PPvtWlo2541jkmW+J7ULLX9mbtO//77L3bv3o1bt27B1dUVbdu2RUREhN49yeujIdWJ66/+xFJjoq4DpVJp9LOStWlIJ+Bqcrkca9aswaxZs8xyd6yqqh2kVB02tYZYJ8CyJ5CWjAU0nH1XzVLrzxJxLL3u1My9TVhy321oxz5LHmOBhnc8smSshrbtAdY5JjWkbcJScSx5MVStIdWJ66/+xAKYqNuMhnQCrlZeXi52iNdQNMQ6AQ3zZMFSGurJviXiWHrdkeEsdeyz9DG2oR2PGiJLbhM8Jtk+S14MtZSGeIHXkhry+mOibkMa0gk40b2E+5ThuO6IyJbwmEREtoKJOhEREREREZEN4evZiIiIiIiIiGwIE3UiIiIiIiIiG8JEnYiIiIiIiMiGMFEnIiIiIiIisiFM1ImIiIiIiIhsCN8/QURE1MCdP38esbGxePnll9GnTx+d5rl06RJ+/PFHpKSkID8/H/b29vD390eXLl0QGRmJZs2ambnUd6xduxYXLlzA2rVrLRKPiIjIFjBRJyIiIg1btmzBzp07ERYWhnHjxqFJkyZQqVS4evUqjhw5gsTERGzZsgV2dmyYR0REZA5M1ImIiEh0/Phx7Ny5E4MHD8aMGTMgkUjEzzp16oSRI0ciKSmpzuWUlZXB2dnZnEUlIiJqsJioExER2aiCggJs3rwZ586dQ35+PlxdXREYGIgJEyagU6dOmDlzJtq3b4+ZM2dqzLd48WKN/9XKy8vx9ddf4/jx4yguLkZoaCiio6MRHBwsTrNz5040atQITz75pEaSriaRSDB06NBq8QoLCzF9+nRs2rQJ6enp6NGjB/73v//hxIkTOHjwIK5du4bbt28jICAAPXr0wLhx4+Di4qKxnMOHD2PXrl3IyclB48aNMXr0aK3rRalUYvfu3Th27Biys7Ph6uqK7t2747HHHoOnp6eOa5eIiMh2MVEnIiKyUWvWrEFaWhomTZqEwMBA3L59G2lpaSgqKjJoeZs3b0ZwcDCee+45FBcX47vvvsPixYvx1ltvoXHjxpDL5cjIyEDfvn3h5OSk17Lz8vKwZs0ajBo1CpMnTxaT/Bs3bqBr164YPnw4XFxckJmZid27d+Py5ctYtGiROP/hw4fx0UcfoUePHnjiiSfE8lVUVGg0sVepVHjrrbdw8eJFjBo1CmFhYZDJZNi2bRsWL16MlStX6l12IiIiW8NEnYiIyEalpKQgIiICgwYNEsf17NnT4OV5enritddeE5Potm3b4qWXXsKuXbvw3HPPITc3FwDg7+9fbV6VSgVBEMS/7ezsNO64FxUV4eWXX0bHjh015hs3bpw4LAgC2rRpg2bNmmHx4sW4evUqWrZsCZVKJV5E0FY+qVQqLuPkyZM4d+4cXnnlFfTu3Vsc37JlS8ybNw+HDx/GkCFDDF5HREREtoCJOhERkY0KDQ3FkSNH0KhRI9x///0ICQmBg4PhP939+vXTSK79/f3Rpk0bnD9/vs55n3rqKRQXF4t/392DvLu7e7UkHQBu3bqFLVu24O+//0ZBQYFGsp+ZmYmWLVsiKysLeXl5GDlypNby5eTkiOPOnj0Ld3d3dO/eHZWVleL4Vq1awdvbG+fPn2eiTkRE9R4TdSIiIhv1v//9Dzt37sTBgwexdetWuLi4oFevXnjsscfg7e2t9/K0zePt7Y2rV68CAHx9fQFAIzFWW7x4MSorK5Gamop169ZV+9zHx6fauNLSUixcuBBOTk6YNGkSmjZtCmdnZ+Tm5uLtt99GeXk5AIhN+WsqX9Xy5Ofn4/bt25gyZYrWOhYWFmodT0REVJ8wUSciIrJRnp6eiI6ORnR0NGQyGc6cOYONGzciPz8fb775JhwdHVFRUVFtvsLCQjRq1KjaeIVCoXWch4cHAEAqlaJ58+ZITk5GeXm5xrPerVq1AnAn+dZGW8dzf//9N/Ly8rB48WK0b99eHH/79m2N6dTxaypfVY0aNUKjRo0wf/58reVwdXXVOp6IiKg+4QtQiYiI6gE/Pz8MHToUnTp1QlpaGoA7TcOvXbumMV1WVhaysrK0LuOXX37RaHqek5ODlJQUdOjQQRw3ZswYFBYW4ptvvtGY1hh3N9c/cOCAxt+BgYHw8fGpsXxVde/eHYWFhVCpVGjdunW1f4GBgSYpMxERkTXxjjoREZENKi4uRmxsLPr27YtmzZrB1dUVly9fxrlz58RO1B566CGsWbMGn3/+OXr37o2cnBwkJCTU+Iqy/Px8xMfHY9CgQSguLsa2bdvg5OSk8Rq0fv36ISMjAzt37sTVq1fRv39/NG3aFIIgQCaT4dixYwBQ7dVq2rRp0wbu7u5Yt24dJkyYAHt7exw7dkxsaq9mZ2eHRx99FJ988olYvtu3b+O7776r1hy+b9++OH78OFasWIHhw4cjNDQU9vb2yM3Nxfnz59GzZ0/06tVLjzVNRERke5ioExER2SBHR0eEhoaK7wqvrKyEn58fRo0ahVGjRgG4k1Tn5eXhp59+wqFDh9CiRQvMmDED27dv17rMyZMn48qVK/joo49QUlKC0NBQ/O9//0OTJk00pps0aRI6d+6MpKQk7NixAwqFAg4ODvD390f79u0xdepUhISE1FmHRo0aYd68efjmm2+wZs0aODs7i+9Xf+ONNzSmjYiIAADs3r0bb7/9Nvz9/TFmzBhcuHABFy5cEKezs7PD66+/jr179+Lo0aPYtWsX7O3t4evri3bt2qFFixZ6rWciIiJbJBFM1a6NiIiIiIiIiIzGZ9SJiIiIiIiIbAgTdSIiIiIiIiIbwkSdiIiIiIiIyIYwUSciIiIiIiKyIUzUiYiIiIiIiGwIE3UiIiIiIiIiG8JEnYiIiIiIiMiGMFEnIiIiIiIisiFM1ImIiIiIiIhsCBN1IiIiIiIiIhvCRJ2IiIiIiIjIhjBRJyIiIiIiIrIh/wcJQCmrLK19uAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 选择类别型列\n",
    "cat_cols = ['grade', 'subGrade', 'employmentLength', 'homeOwnership', 'verificationStatus', 'purpose', 'regionCode']\n",
    "\n",
    "# 绘制类别分布\n",
    "for col in cat_cols[:3]:\n",
    "    plt.figure(figsize=(12,4))\n",
    "    sns.countplot(x=col, data=train_df, order=train_df[col].value_counts().index)\n",
    "    plt.xticks(rotation=45)\n",
    "    plt.title(f'Distribution of {col}')\n",
    "    plt.show()\n",
    "\n",
    "# 类别与目标的关系\n",
    "for col in cat_cols[:2]:\n",
    "    plt.figure(figsize=(12,4))\n",
    "    sns.barplot(x=col, y='isDefault', data=train_df)\n",
    "    plt.title(f'Default Rate by {col}')\n",
    "    plt.xticks(rotation=45)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "27344f01",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 数值列填充\n",
    "num_cols = train_df.select_dtypes(include=['int64','float64']).columns.difference(['id','isDefault'])\n",
    "train_df[num_cols] = train_df[num_cols].fillna(train_df[num_cols].median())\n",
    "test_df[num_cols] = test_df[num_cols].fillna(train_df[num_cols].median())  # 保持与训练集一致\n",
    "\n",
    "# 类别列填充\n",
    "train_df['employmentLength'] = train_df['employmentLength'].fillna('unknown')\n",
    "test_df['employmentLength'] = test_df['employmentLength'].fillna('unknown')\n",
    "\n",
    "# 其他文本列（如purpose）如果有缺失值\n",
    "train_df['purpose'] = train_df['purpose'].fillna('other')\n",
    "test_df['purpose'] = test_df['purpose'].fillna('other')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "96bdd5cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 确保已从train_df中分离特征和目标列\n",
    "# 排除不需要的列\n",
    "exclude_cols = ['id', 'isDefault', 'earliesCreditLine', 'issueDate', 'title'] \n",
    "features = [col for col in train_df.columns if col not in exclude_cols]\n",
    "\n",
    "X = train_df[features]  # 特征矩阵\n",
    "y = train_df['isDefault']  # 目标变量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "be8c9ee3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练集形状: (640000, 42), 验证集形状: (160000, 42)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# 按8:2划分，并保持类别比例\n",
    "X_train, X_val, y_train, y_val = train_test_split(\n",
    "    X, \n",
    "    y, \n",
    "    test_size=0.2, \n",
    "    stratify=y, \n",
    "    random_state=42\n",
    ")\n",
    "\n",
    "print(f\"训练集形状: {X_train.shape}, 验证集形状: {X_val.shape}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "bfb5d4f5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "数值型特征: 39个\n",
      " ['loanAmnt', 'term', 'interestRate', 'installment', 'employmentTitle', 'homeOwnership', 'annualIncome', 'verificationStatus', 'purpose', 'postCode', 'regionCode', 'dti', 'delinquency_2years', 'ficoRangeLow', 'ficoRangeHigh', 'openAcc', 'pubRec', 'pubRecBankruptcies', 'revolBal', 'revolUtil', 'totalAcc', 'initialListStatus', 'applicationType', 'policyCode', 'n0', 'n1', 'n2', 'n3', 'n4', 'n5', 'n6', 'n7', 'n8', 'n9', 'n10', 'n11', 'n12', 'n13', 'n14']\n",
      "\n",
      "类别型特征: 3个\n",
      " ['grade', 'subGrade', 'employmentLength']\n"
     ]
    }
   ],
   "source": [
    "# 获取数值型列（自动排除object类型）\n",
    "num_cols = X_train.select_dtypes(include=['int64', 'float64']).columns\n",
    "cat_cols = X_train.select_dtypes(include=['object', 'category']).columns\n",
    "\n",
    "print(f\"数值型特征: {len(num_cols)}个\\n\", num_cols.tolist())\n",
    "print(f\"\\n类别型特征: {len(cat_cols)}个\\n\", cat_cols.tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "635967fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "保留的数值特征: ['loanAmnt', 'term', 'interestRate', 'installment', 'employmentTitle', 'homeOwnership', 'annualIncome', 'verificationStatus', 'purpose', 'postCode', 'regionCode', 'dti', 'delinquency_2years', 'ficoRangeLow', 'ficoRangeHigh', 'openAcc', 'pubRec', 'pubRecBankruptcies', 'revolBal', 'revolUtil', 'totalAcc', 'initialListStatus', 'applicationType', 'n0', 'n1', 'n2', 'n3', 'n4', 'n5', 'n6', 'n7', 'n8', 'n9', 'n10', 'n13', 'n14']\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_selection import VarianceThreshold\n",
    "\n",
    "# 仅对数值列操作\n",
    "selector = VarianceThreshold(threshold=0.01)\n",
    "X_train_num_selected = selector.fit_transform(X_train[num_cols])\n",
    "X_val_num_selected = selector.transform(X_val[num_cols])\n",
    "\n",
    "# 获取被保留的数值特征名\n",
    "selected_num_features = num_cols[selector.get_support()].tolist()\n",
    "print(\"保留的数值特征:\", selected_num_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b4831e94",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import OrdinalEncoder\n",
    "\n",
    "# 对类别列进行序号编码\n",
    "encoder = OrdinalEncoder(handle_unknown='use_encoded_value', unknown_value=-1)\n",
    "X_train_cat_encoded = encoder.fit_transform(X_train[cat_cols])\n",
    "X_val_cat_encoded = encoder.transform(X_val[cat_cols])\n",
    "\n",
    "# 转换为DataFrame保留列名\n",
    "cat_encoded_cols = [f\"cat_{col}\" for col in cat_cols]\n",
    "X_train_cat_encoded = pd.DataFrame(X_train_cat_encoded, columns=cat_encoded_cols)\n",
    "X_val_cat_encoded = pd.DataFrame(X_val_cat_encoded, columns=cat_encoded_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "65bd4156",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "最终特征矩阵形状: (640000, 39)\n"
     ]
    }
   ],
   "source": [
    "# 水平拼接\n",
    "X_train_processed = pd.concat([\n",
    "    pd.DataFrame(X_train_num_selected, columns=selected_num_features),\n",
    "    X_train_cat_encoded\n",
    "], axis=1)\n",
    "\n",
    "X_val_processed = pd.concat([\n",
    "    pd.DataFrame(X_val_num_selected, columns=selected_num_features),\n",
    "    X_val_cat_encoded\n",
    "], axis=1)\n",
    "\n",
    "print(\"最终特征矩阵形状:\", X_train_processed.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7b1288e6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "验证集AUC: 0.7137\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "# 初始化模型\n",
    "model = RandomForestClassifier(\n",
    "    n_estimators=200,\n",
    "    max_depth=10,\n",
    "    min_samples_leaf=5,\n",
    "    class_weight='balanced',  # 处理类别不平衡\n",
    "    random_state=42,\n",
    "    n_jobs=-1  # 使用所有CPU核心\n",
    ")\n",
    "\n",
    "# 训练模型\n",
    "model.fit(X_train_processed, y_train)\n",
    "\n",
    "# 验证集预测\n",
    "y_val_pred = model.predict_proba(X_val_processed)[:, 1]  # 获取正类的概率\n",
    "\n",
    "# 计算AUC\n",
    "val_auc = roc_auc_score(y_val, y_val_pred)\n",
    "\n",
    "print(f\"验证集AUC: {val_auc:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "526036ca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "测试集处理后的形状: (200000, 39)\n"
     ]
    }
   ],
   "source": [
    "# 1. 数值特征选择使用训练集相同的selector\n",
    "X_test_num_selected = selector.transform(test_df[num_cols])\n",
    "\n",
    "# 2. 类别特征编码使用训练集相同的encoder\n",
    "X_test_cat_encoded = encoder.transform(test_df[cat_cols])\n",
    "X_test_cat_encoded = pd.DataFrame(X_test_cat_encoded, columns=cat_encoded_cols)\n",
    "\n",
    "# 3. 合并测试集特征\n",
    "X_test_processed = pd.concat([\n",
    "    pd.DataFrame(X_test_num_selected, columns=selected_num_features),\n",
    "    X_test_cat_encoded\n",
    "], axis=1)\n",
    "print(\"\\n测试集处理后的形状:\", X_test_processed.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ac32b44c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "模型和预处理对象已保存到: ./深度学习\\models\n"
     ]
    }
   ],
   "source": [
    "import joblib\n",
    "import os\n",
    "\n",
    "# 创建模型保存目录\n",
    "model_dir = os.path.join(DATA_DIR, \"models\")\n",
    "os.makedirs(model_dir, exist_ok=True)\n",
    "\n",
    "# 保存模型和预处理对象\n",
    "joblib.dump(model, os.path.join(model_dir, \"random_forest_model.pkl\"))\n",
    "joblib.dump(selector, os.path.join(model_dir, \"feature_selector.pkl\"))\n",
    "joblib.dump(encoder, os.path.join(model_dir, \"category_encoder.pkl\"))\n",
    "\n",
    "print(f\"\\n模型和预处理对象已保存到: {model_dir}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d7599470",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test_pred = model.predict_proba(X_test_processed)[:, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7b2c9dcd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "样本提交文件要求:\n",
      "       id  isDefault\n",
      "0  800000        0.5\n",
      "1  800001        0.5\n",
      "2  800002        0.5\n",
      "3  800003        0.5\n",
      "4  800004        0.5\n",
      "\n",
      "提交文件已保存到: ./深度学习\\submission.csv\n",
      "文件预览:\n",
      "       id  isDefault\n",
      "0  800000   0.318619\n",
      "1  800001   0.601538\n",
      "2  800002   0.698323\n",
      "3  800003   0.590376\n",
      "4  800004   0.613216\n"
     ]
    }
   ],
   "source": [
    "# 确保ID列存在\n",
    "if 'id' not in test_df.columns:\n",
    "    test_df['id'] = range(len(test_df))\n",
    "\n",
    "# 创建提交DataFrame\n",
    "submit_df = pd.DataFrame({\n",
    "    'id': test_df['id'],\n",
    "    'isDefault': y_test_pred  # 注意比赛要求可能是概率或类别\n",
    "})\n",
    "\n",
    "# 检查样本提交文件格式\n",
    "if 'sample' in file_paths:\n",
    "    sample_submit = pd.read_csv(file_paths[\"sample\"])\n",
    "    print(\"\\n样本提交文件要求:\")\n",
    "    print(sample_submit.head())\n",
    "    \n",
    "    # 如果样本要求类别而不是概率，则转换\n",
    "    if sample_submit['isDefault'].dtype == 'int64':\n",
    "        submit_df['isDefault'] = (y_test_pred > 0.5).astype(int)\n",
    "        print(\"\\n注意: 已将概率转换为类别预测\")\n",
    "\n",
    "# 保存提交文件\n",
    "submit_path = os.path.join(DATA_DIR, \"submission.csv\")\n",
    "submit_df.to_csv(submit_path, index=False)\n",
    "print(f\"\\n提交文件已保存到: {submit_path}\")\n",
    "print(\"文件预览:\")\n",
    "print(submit_df.head())"
   ]
  }
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